{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T07:31:36Z","timestamp":1771572696708,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T00:00:00Z","timestamp":1668470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Breast cancer is the type of cancer with the highest incidence and global mortality of female cancers. Thus, the adaptation of modern technologies that assist in medical diagnosis in order to accelerate, automate and reduce the subjectivity of this process are of paramount importance for an efficient treatment. Therefore, this work aims to propose a robust platform to compare and evaluate the proposed strategies for improving breast ultrasound images and compare them with state-of-the-art techniques by classifying them as benign, malignant and normal. Investigations were performed on a dataset containing a total of 780 images of tumor-affected persons, divided into benign, malignant and normal. A data augmentation technique was used to scale up the corpus of images available in the chosen dataset. For this, novel image enhancement techniques were used and the Multilayer Perceptrons, k-Nearest Neighbor and Support Vector Machines algorithms were used for classification. From the promising outcomes of the conducted experiments, it was observed that the bilateral algorithm together with the SVM classifier achieved the best result for the classification of breast cancer, with an overall accuracy of 96.69% and an accuracy for the detection of malignant nodules of 95.11%. Therefore, it was found that the application of image enhancement methods can help in the detection of breast cancer at a much earlier stage with better accuracy in detection.<\/jats:p>","DOI":"10.3390\/s22228818","type":"journal-article","created":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T04:39:03Z","timestamp":1668573543000},"page":"8818","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Mammogram Image Enhancement Techniques for Online Breast Cancer Detection and Diagnosis"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5670-1496","authenticated-orcid":false,"given":"Daniel S.","family":"da Silva","sequence":"first","affiliation":[{"name":"Department of Teleinformatics Engineering, Federal University of Cear\u00e1, Fortaleza 60455-970, CE, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4665-9225","authenticated-orcid":false,"given":"Caio S.","family":"Nascimento","sequence":"additional","affiliation":[{"name":"Department of Teleinformatics Engineering, Federal University of Cear\u00e1, Fortaleza 60455-970, CE, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9516-0327","authenticated-orcid":false,"given":"Senthil K.","family":"Jagatheesaperumal","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi 626005, TN, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3886-4309","authenticated-orcid":false,"given":"Victor Hugo C. de","family":"Albuquerque","sequence":"additional","affiliation":[{"name":"Department of Teleinformatics Engineering, Federal University of Cear\u00e1, Fortaleza 60455-970, CE, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, M. (2021, January 14\u201316). Research on the Detection Method of Breast Cancer Deep Convolutional Neural Network Based on Computer Aid. Proceedings of the 2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC), IEEE, Dalian, China.","DOI":"10.1109\/IPEC51340.2021.9421338"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Matic, Z., and Kadry, S. (2022, January 28\u201329). Tumor Segmentation in Breast MRI Using Deep Learning. Proceedings of the 2022 Fifth International Conference of Women in Data Science at Prince Sultan University (WiDS PSU), Riyadh, Saudi Arabia.","DOI":"10.1109\/WiDS-PSU54548.2022.00021"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ahmed, M., and Islam, M.R. (2021, January 26\u201327). Breast Cancer Classification from Histopathological Images using Convolutional Neural Network. Proceedings of the 2021 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2), Rajshahi, Bangladesh.","DOI":"10.1109\/IC4ME253898.2021.9768615"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Khumdee, M., Assawaroongsakul, P., Phasukkit, P., and Houngkamhang, N. (2021, January 21\u201323). Breast Cancer Detection using IR-UWB with Deep Learning. Proceedings of the 2021 16th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), Ayutthaya, Thailand.","DOI":"10.1109\/iSAI-NLP54397.2021.9678158"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Afaq, S., and Jain, A. (2022, January 20\u201321). MAMMO-Net: An Approach for Classification of Breast Cancer using CNN with Gabor Filter in Mammographic Images. Proceedings of the 2022 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), Greater Noida, India.","DOI":"10.1109\/CISES54857.2022.9844320"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1109\/TMI.2019.2936500","article-title":"Deeply-Supervised Networks With Threshold Loss for Cancer Detection in Automated Breast Ultrasound","volume":"39","author":"Wang","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wu, H., Huo, Y., Pan, Y., Xu, Z., Huang, R., Xie, Y., Han, C., Liu, Z., and Wang, Y. (2022, January 21\u201323). Learning Pre- and Post-contrast Representation for Breast Cancer Segmentation in DCE-MRI. Proceedings of the 2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS), Shenzhen, China.","DOI":"10.1109\/CBMS55023.2022.00070"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1109\/TUFFC.2019.2918180","article-title":"Debiasing-Based Noise Suppression for Ultrafast Ultrasound Microvessel Imaging","volume":"66","author":"Huang","year":"2019","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Moshrefi, A., and Nabki, F. (2021, January 9\u201311). An Efficient Method to Enhance the Quality of Ultrasound Medical Images. Proceedings of the 2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), Lansing, MI, USA.","DOI":"10.1109\/MWSCAS47672.2021.9531764"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"104260","DOI":"10.1016\/j.compbiomed.2021.104260","article-title":"Computer-assisted Parkinson\u2019s disease diagnosis using fuzzy optimum- path forest and Restricted Boltzmann Machines","volume":"131","author":"Silva","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Qadri, S.F., Shen, L., Ahmad, M., Qadri, S., Zareen, S.S., and Akbar, M.A. (2022). SVseg: Stacked sparse autoencoder-based patch classification modeling for vertebrae segmentation. Mathematics, 10.","DOI":"10.3390\/math10050796"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.future.2018.11.054","article-title":"A recurrence plot-based approach for Parkinson\u2019s disease identification","volume":"94","author":"Afonso","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.neunet.2019.11.017","article-title":"Learning physical properties in complex visual scenes: An intelligent machine for perceiving blood flow dynamics from static CT angiography imaging","volume":"123","author":"Gao","year":"2020","journal-title":"Neural Netw."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4267","DOI":"10.1109\/JBHI.2021.3067789","article-title":"Multi-Class Skin Lesion Detection and Classification via Teledermatology","volume":"25","author":"Khan","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2264","DOI":"10.1109\/TNSRE.2022.3198434","article-title":"An Effective Fusing Approach by Combining Connectivity Network Pattern and Temporal-Spatial Analysis for EEG-Based BCI Rehabilitation","volume":"30","author":"Cao","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chen, J., Zheng, Y., Liang, Y., Zhan, Z., Jiang, M., Zhang, X., Daniel, S.d.S., Wu, W., and Albuquerque, V.H.C.d. (2022). Edge2Analysis: A Novel AIoT Platform for Atrial Fibrillation Recognition and Detection. IEEE J. Biomed. Health Inform.","DOI":"10.1109\/JBHI.2022.3171918"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"107911","DOI":"10.1016\/j.compeleceng.2022.107911","article-title":"A novel method for lung nodule detection in computed tomography scans based on Boolean equations and vector of filters techniques","volume":"100","author":"Cortez","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"7853","DOI":"10.1109\/TII.2022.3149939","article-title":"An Intelligent Multisampling Tensor Model for Oral Cancer Classification","volume":"18","author":"Huang","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mohamed, A., Wahba, A.A., Sayed, A.M., Haggag, M.A., and El-Adawy, M.I. (2019, January 2\u20134). Enhancement of Ultrasound Images Quality Using a New Matching Material. Proceedings of the 2019 International Conference on Innovative Trends in Computer Engineering (ITCE), Aswan, Egypt.","DOI":"10.1109\/ITCE.2019.8646468"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Singh, P., Mukundan, R., and Ryke, R.d. (2019, January 28\u201330). Feature Enhancement in Medical Ultrasound Videos Using Multifractal and Contrast Adaptive Histogram Equalization Techniques. Proceedings of the 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), San Jose, CA, USA.","DOI":"10.1109\/MIPR.2019.00050"},{"key":"ref_21","unstructured":"Latif, G., Butt, M., Al Anezi, F.Y., and Alghazo, J. (2020, January 14\u201315). Remo\u00e7\u00e3o de manchas de imagem por ultrassom e detec\u00e7\u00e3o de c\u00e2ncer de mama usando Deep CNN. Proceedings of the 2020 RIVF International Conference on Computing and Communication Technologies (RIVF), Ho Chi Minh City, Vietnam."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhao, H., Niu, J., Meng, H., Wang, Y., Li, Q., and Yu, Z. (2022, January 11\u201315). Focal U-Net: A Focal Self-attention based U-Net for Breast Lesion Segmentation in Ultrasound Images. Proceedings of the 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, UK.","DOI":"10.1109\/EMBC48229.2022.9870824"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jahwar, A.F., and Mohsin Abdulazeez, A. (2022, January 12). Segmentation and Classification for Breast Cancer Ultrasound Images Using Deep Learning Techniques: A Review. Proceedings of the 2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA), Selangor, Malaysia.","DOI":"10.1109\/CSPA55076.2022.9781824"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Dabass, J., Arora, S., Vig, R., and Hanmandlu, M. (2019, January 10\u201311). Segmentation Techniques for Breast Cancer Imaging Modalities-A Review. Proceedings of the 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India.","DOI":"10.1109\/CONFLUENCE.2019.8776937"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2439","DOI":"10.1109\/TMI.2021.3078370","article-title":"Domain Knowledge Powered Deep Learning for Breast Cancer Diagnosis Based on Contrast-Enhanced Ultrasound Videos","volume":"40","author":"Chen","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Badawy, S.M., Mohamed, A.E.N.A., Hefnawy, A.A., Zidan, H.E., GadAllah, M.T., and El-Banby, G.M. (2021, January 13\u201315). Classification of Breast Ultrasound Images Based on Convolutional Neural Networks\u2014A Comparative Study. Proceedings of the 2021 International Telecommunications Conference (ITC-Egypt), Alexandria, Egypt.","DOI":"10.1109\/ITC-Egypt52936.2021.9513972"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"27779","DOI":"10.1109\/ACCESS.2020.2964276","article-title":"Breast Cancer Image Classification via Multi-Network Features and Dual-Network Orthogonal Low-Rank Learning","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yaganteeswarudu, A. (2020, January 10\u201312). Multi Disease Prediction Model by using Machine Learning and Flask API. Proceedings of the 2020 5th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India.","DOI":"10.1109\/ICCES48766.2020.9137896"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Mufid, M.R., Basofi, A., Al Rasyid, M.U.H., Rochimansyah, I.F., and rokhim, A. (2019, January 27\u201328). Design an MVC Model using Python for Flask Framework Development. Proceedings of the 2019 International Electronics Symposium (IES), Surabaya, Indonesia.","DOI":"10.1109\/ELECSYM.2019.8901656"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"15855","DOI":"10.1109\/JIOT.2020.3034074","article-title":"An IoT-Based Deep Learning Framework for Early Assessment of Covid-19","volume":"8","author":"Ahmed","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"217897","DOI":"10.1109\/ACCESS.2020.3041867","article-title":"Image Enhancement for Tuberculosis Detection Using Deep Learning","volume":"8","author":"Munadi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"104863","DOI":"10.1016\/j.dib.2019.104863","article-title":"Dataset of breast ultrasound images","volume":"28","author":"Gomaa","year":"2020","journal-title":"Data Brief"},{"key":"ref_33","unstructured":"Tomasi, C., and Manduchi, R. (1998, January 7). Bilateral filtering for gray and color images. Proceedings of the Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), Bombay, India."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhihong, W., and Xiaohong, X. (2011, January 22\u201323). Study on Histogram Equalization. Proceedings of the 2011 2nd International Symposium on Intelligence Information Processing and Trusted Computing, Wuhan, China.","DOI":"10.1109\/IPTC.2011.52"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1023\/B:JMIV.0000011321.19549.88","article-title":"An algorithm for total variation minimization and applications","volume":"20","author":"Chambolle","year":"2004","journal-title":"J. Math. Imaging Vis."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/TIP.2016.2639450","article-title":"LIME: Low-Light Image Enhancement via Illumination Map Estimation","volume":"26","author":"Guo","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Felsberg, M., Heyden, A., and Kr\u00fcger, N. (2017, January 22\u201324). A New Image Contrast Enhancement Algorithm Using Exposure Fusion Framework. Proceedings of the Computer Analysis of Images and Patterns, Ystad, Sweden.","DOI":"10.1007\/978-3-319-64689-3"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.compeleceng.2017.09.012","article-title":"Contrast enhancement of brightness-distorted images by improved adaptive gamma correction","volume":"66","author":"Cao","year":"2018","journal-title":"Comput. Electr. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"8968","DOI":"10.1109\/TIP.2021.3116790","article-title":"Light-DehazeNet: A Novel Lightweight CNN Architecture for Single Image Dehazing","volume":"30","author":"Ullah","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Guo, C., Li, C., Guo, J., Loy, C.C., Hou, J., Kwong, S., and Cong, R. (2020, January 13\u201319). Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00185"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wan, R., Yang, W., Li, H., Chau, L.P., and Kot, A.C. (2021). Low-Light Image Enhancement with Normalizing Flow. arXiv.","DOI":"10.1609\/aaai.v36i3.20162"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4455","DOI":"10.1109\/JIOT.2019.2950469","article-title":"Cost-Effective Video Summarization Using Deep CNN With Hierarchical Weighted Fusion for IoT Surveillance Networks","volume":"7","author":"Muhammad","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"9237","DOI":"10.1109\/JIOT.2019.2896120","article-title":"Energy-Efficient Deep CNN for Smoke Detection in Foggy IoT Environment","volume":"6","author":"Khan","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1109\/TII.2019.2929228","article-title":"Cloud-Assisted Multiview Video Summarization Using CNN and Bidirectional LSTM","volume":"16","author":"Hussain","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1016\/j.future.2021.06.045","article-title":"Human action recognition using attention based LSTM network with dilated CNN features","volume":"125","author":"Muhammad","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Liu, X., Wu, Z., and Tang, C. (2021, January 27\u201328). Modulation Recognition Algorithm Based on ResNet50 Multi-feature Fusion. Proceedings of the 2021 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), Xi\u2019an, China.","DOI":"10.1109\/ICITBS53129.2021.00171"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Yang, X., Yang, D., and Huang, C. (2021, January 15\u201317). An interactive prediction system of breast cancer based on ResNet50, chatbot and PyQt. Proceedings of the 2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT), Shanghai, China.","DOI":"10.1109\/AINIT54228.2021.00068"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wan, Z., and Gu, T. (2021, January 24\u201326). A Bootstrapped Transfer Learning Model Based on ResNet50 and Xception to Classify Buildings Post Hurricane. Proceedings of the 2021 2nd International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), Zhuhai, China.","DOI":"10.1109\/ICBASE53849.2021.00055"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhao, Z., He, J., Zhu, Y., and Wei, X. (2021, January 14\u201316). A method of vehicle flow training and detection based on ResNet50 with CenterNet method. Proceedings of the 2021 International Conference on Communications, Information System and Computer Engineering (CISCE), Beijing, China.","DOI":"10.1109\/CISCE52179.2021.9446012"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Dutta, J., and Chanda, D. (2021, January 25\u201317). Music Emotion Recognition in Assamese Songs using MFCC Features and MLP Classifier. Proceedings of the 2021 International Conference on Intelligent Technologies (CONIT), Hubli, India.","DOI":"10.1109\/CONIT51480.2021.9498345"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ahil, M.N., Vanitha, V., and Rajathi, N. (2021, January 8\u20139). Apple and Grape Leaf Disease Classification using MLP and CNN. Proceedings of the 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), Coimbatore, India.","DOI":"10.1109\/ICAECA52838.2021.9675567"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wang, H., and Wang, J. (2021, January 6\u20138). Short Term Wind Speed Forecasting Based on Feature Extraction by CNN and MLP. Proceedings of the 2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC), Nanjing, China.","DOI":"10.1109\/ISCEIC53685.2021.00047"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Liu, H., Xiao, X., Li, Y., Mi, Q., and Yang, Z. (2019, January 3\u20135). Effective Data Classification via Combining Neural Networks and SVM. Proceedings of the 2019 Chinese Control And Decision Conference (CCDC), Nanchang, China.","DOI":"10.1109\/CCDC.2019.8832442"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Kr, K., Kv, A.R., and Pillai, A. (2019, January 5\u20136). An Improved Feature Selection and Classification of Gene Expression Profile using SVM. Proceedings of the 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Kannur, India.","DOI":"10.1109\/ICICICT46008.2019.8993358"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Turesson, H.K., Ribeiro, S., Pereira, D.R., Papa, J.P., and de Albuquerque, V.H.C. (2016). Machine learning algorithms for automatic classification of marmoset vocalizations. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0163041"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Salim, A.P., Laksitowening, K.A., and Asror, I. (2020, January 24\u201326). Time Series Prediction on College Graduation Using KNN Algorithm. Proceedings of the 2020 8th International Conference on Information and Communication Technology (ICoICT), Yogyakarta, Indonesia.","DOI":"10.1109\/ICoICT49345.2020.9166238"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Ma, J., Li, J., and Wang, W. (2022, January 27\u201329). Application of Wavelet Entropy and KNN in Motor Fault Diagnosis. Proceedings of the 2022 4th International Conference on Communications, Information System and Computer Engineering (CISCE), Shenzhen, China.","DOI":"10.1109\/CISCE55963.2022.9851059"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Sabilla, I.A., Meirisdiana, M., Sunaryono, D., and Husni, M. (2021, January 14\u201315). Best Ratio Size of Image in Steganography using Portable Document Format with Evaluation RMSE, PSNR, and SSIM. Proceedings of the 2021 4th International Conference of Computer and Informatics Engineering (IC2IE), Depok, Indonesia.","DOI":"10.1109\/IC2IE53219.2021.9649198"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Rodriguez-Molares, A., Hoel Rindal, O.M., D\u2019hooge, J., M\u00e5s\u00f8y, S.E., Austeng, A., and Torp, H. (2018, January 22\u201325). The Generalized Contrast-to-Noise Ratio. Proceedings of the 2018 IEEE International Ultrasonics Symposium (IUS), Kobe, Japan.","DOI":"10.1109\/ULTSYM.2018.8580101"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Harun, N.H., Bakar, J.A., Wahab, Z.A., Osman, M.K., and Harun, H. (2020, January 18\u201319). Color Image Enhancement of Acute Leukemia Cells in Blood Microscopic Image for Leukemia Detection Sample. Proceedings of the 2020 IEEE 10th Symposium on Computer Applications & Industrial Electronics (ISCAIE), Penang, Malaysia.","DOI":"10.1109\/ISCAIE47305.2020.9108810"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"AbdAlRahman, A., Ismail, S.M., Said, L.A., and Radwan, A.G. (2021, January 23\u201325). Double Fractional-order Masks Image Enhancement. Proceedings of the 2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES), Giza, Egypt.","DOI":"10.1109\/NILES53778.2021.9600543"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8818\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:18:20Z","timestamp":1760145500000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8818"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,15]]},"references-count":62,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22228818"],"URL":"https:\/\/doi.org\/10.3390\/s22228818","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,15]]}}}