{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:19:10Z","timestamp":1772821150888,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:00Z","timestamp":1671062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>In recent years, skin cancer diagnosis has been aided by the most sophisticated and advanced machine learning algorithms, primarily implemented in the spatial domain. In this research work, we concentrated on two crucial phases of a computer-aided diagnosis system: (i) image enhancement through enhanced median filtering algorithms based on the range method, fuzzy relational method, and similarity coefficient, and (ii) wavelet decomposition using DB4, Symlet, RBIO, and extracting seven unique entropy features and eight statistical features from the segmented image. The extracted features were then normalized and provided for classification based on supervised and deep-learning algorithms. The proposed system is comprised of enhanced filtering algorithms, Normalized Otsu\u2019s Segmentation, and wavelet-based entropy. Statistical feature extraction led to a classification accuracy of 93.6%, 0.71% higher than the spatial domain-based classification. With better classification accuracy, the proposed system will assist clinicians and dermatology specialists in identifying skin cancer early in its stages.<\/jats:p>","DOI":"10.3390\/info13120583","type":"journal-article","created":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T02:54:02Z","timestamp":1671159242000},"page":"583","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Wavelet-Based Classification of Enhanced Melanoma Skin Lesions through Deep Neural Architectures"],"prefix":"10.3390","volume":"13","author":[{"given":"Premaladha","family":"Jayaraman","sequence":"first","affiliation":[{"name":"School of Computing, SASTRA Deemed University, Thanjavur 613401, TN, India"}]},{"given":"Nirmala","family":"Veeramani","sequence":"additional","affiliation":[{"name":"School of Computing, SASTRA Deemed University, Thanjavur 613401, TN, India"}]},{"given":"Raghunathan","family":"Krishankumar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Amrita School of Computing, Coimbatore 641112, Amrita Vishwa Vidyapeetham, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2397-461X","authenticated-orcid":false,"given":"Kattur Soundarapandian","family":"Ravichandran","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Amrita School of Physical Sciences, Coimbatore 602105, Amrita Vishwa Vidyapeetham, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4533-1025","authenticated-orcid":false,"given":"Fausto","family":"Cavallaro","sequence":"additional","affiliation":[{"name":"Department of Economics, University of Molise, 86100 Campobasso, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9186-4167","authenticated-orcid":false,"given":"Pratibha","family":"Rani","sequence":"additional","affiliation":[{"name":"Department of Engineering Mathematics, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, Andhra Pradesh, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1010-3655","authenticated-orcid":false,"given":"Abbas","family":"Mardani","sequence":"additional","affiliation":[{"name":"Muma Business School, University of South Florida (USF), Tampa, FL 33612, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,15]]},"reference":[{"key":"ref_1","unstructured":"(2021, September 10). Available online: http:\/\/www.cancer.org\/cancer\/skincancer-melanoma\/detailedguide\/melanoma-skin-cancer-key-statistics."},{"key":"ref_2","first-page":"15","article-title":"Artificial Intelligence and Patentability: Review and Discussions","volume":"1","author":"Chatterjee","year":"2021","journal-title":"Int. J. Mod. Res."},{"key":"ref_3","first-page":"1","article-title":"Crime tracking system and people\u2019s safety in India using machine learning approaches","volume":"2","author":"Gupta","year":"2022","journal-title":"Int. J. Mod. Res."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gulati, S., and Bhogal, R.K. (2020). Classification of Melanoma from Dermoscopic Images Using Machine Learning. Smart Intelligent Computing and Applications, Springer.","DOI":"10.1007\/978-981-13-9282-5_32"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.patrec.2019.11.034","article-title":"Developed Newton-Raphson based deep features selection framework for skin lesion recognition","volume":"129","author":"Khan","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.patrec.2020.05.019","article-title":"A new approach for classification skin lesion based on transfer learning, deep learning, and IoT system","volume":"136","author":"Rodrigues","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1555","DOI":"10.31557\/APJCP.2019.20.5.1555","article-title":"Deep Learning Based Skin Lesion Segmentation and Classification of Melanoma Using Support Vector Machine (SVM)","volume":"20","author":"Seeja","year":"2019","journal-title":"Asian Pac. J. Cancer Prev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"23559","DOI":"10.1007\/s11042-019-7652-y","article-title":"DermoDeep-A classification of melanoma-nevus skin lesions using multi-feature fusion of visual features and deep neural network","volume":"78","author":"Abbas","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-016-0460-2","article-title":"Novel Approaches for Diagnosing Melanoma Skin Lesions through Supervised and Deep Learning Algorithms","volume":"40","author":"Premaladha","year":"2016","journal-title":"J. Med. Syst."},{"key":"ref_10","first-page":"1","article-title":"Melanoma recognition using extended set of descriptors and classifiers","volume":"1","author":"Kruk","year":"2015","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s40009-015-0353-9","article-title":"Detection of Melanoma Skin Lesions Using Phylogeny","volume":"38","author":"Premaladha","year":"2015","journal-title":"Natl. Acad. Sci. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Alrashed, F.A., Alsubiheen, A.M., Alshammari, H., Mazi, S.I., Al-Saud, S.A., Alayoubi, S., Kachanathu, S.J., Albarrati, A., Aldaihan, M.M., and Ahmad, T. (2022). Stress, Anxiety, and Depression in Pre-Clinical Medical Students: Prevalence and Association with Sleep Disorders. Sustainability, 14.","DOI":"10.3390\/su141811320"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s40010-015-0200-x","article-title":"Quantification of Fuzzy Borders and Fuzzy Asymmetry of Malignant Melanomas","volume":"85","author":"Premaladha","year":"2015","journal-title":"Proc. Natl. Acad. Sci. India Sect. A Phys. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/s12293-014-0144-8","article-title":"An ensemble classification approach for melanoma diagnosis","volume":"6","author":"Schaefer","year":"2014","journal-title":"Memetic Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1007\/s11517-012-0895-7","article-title":"Distribution quantification on dermoscopy images for computer-assisted diagnosis of cutaneous melanomas","volume":"50","author":"Liu","year":"2012","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_16","first-page":"17","article-title":"Self-aware Execution Environment Model (SAE2) for the Performance Improvement of Multicore Systems","volume":"2","author":"Shukla","year":"2022","journal-title":"Int. J. Mod. Res."},{"key":"ref_17","first-page":"8","article-title":"Breast Cancer Image Classification using Transfer Learning and Convolutional Neural Network","volume":"2","author":"Sharma","year":"2022","journal-title":"Int. J. Mod. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.17485\/ijst\/2015\/v8i22\/79140","article-title":"Normalised Otsu\u2019s Segmentation Algorithm for Melanoma Diagnosis","volume":"8","author":"Premaladha","year":"2015","journal-title":"Indian J. Sci. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.17485\/ijst\/2015\/v8i22\/79318","article-title":"Image Enhancement Techniques: A Study","volume":"8","author":"Janani","year":"2015","journal-title":"Indian J. Sci. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6578","DOI":"10.1016\/j.eswa.2015.04.034","article-title":"MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images","volume":"42","author":"Giotis","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yuan, X., Mart\u00ednez, J.-F., Eckert, M., and L\u00f3pez-Santidri\u00e1n, L. (2016). An Improved Otsu Threshold Segmentation Method for Underwater Simultaneous Localization and Mapping-Based Navigation. Sensors, 16.","DOI":"10.3390\/s16071148"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Surowka, G. (September, January 31). Symbolic learning supporting early diagnosis of melanoma. Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina.","DOI":"10.1109\/IEMBS.2010.5627337"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Surowka, G. (2008, January 25\u201327). Supervised learning of melanocytic skin lesion images. Proceedings of the IEEE Conference on Human System Interactions, Krak\u00f3w, Poland.","DOI":"10.1109\/HSI.2008.4581420"},{"key":"ref_24","unstructured":"Fassihi, N., Shanbehzadeh, J., Sarafzadeh, A., and Ghasemi, E. (2011, January 16\u201318). Melanoma diagnosis by the use of wavelet analysis based on morphological operators. Proceedings of the International Multiconference of Engineers and Computer Scientists, Hong Kong, China."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"D\u2019Alessandro, B., Dhawan, A.P., and Mullani, N. (September, January 30). Computer aided analysis of epi-illumination and transillumination images of skin lesions for diagnosis of skin cancers. Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA.","DOI":"10.1109\/IEMBS.2011.6090929"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1109\/TITB.2012.2212282","article-title":"Computer-aided diagnosis of melanoma using border-and wavelet-based texture analysis","volume":"16","author":"Garnavi","year":"2012","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2297","DOI":"10.1073\/pnas.88.6.2297","article-title":"Approximate entropy as a measure of system complexity","volume":"88","author":"Pincus","year":"1991","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1097\/00000542-200003000-00016","article-title":"Approximate entropy as an electroencephalographic measure of anesthetic drug effect during desflurane anesthesia","volume":"92","author":"Bruhn","year":"2001","journal-title":"Anesthesiology"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Attallah, O., Sharkas, M.A., and Gadelkarim, H. (2020). Deep Learning Techniques for Automatic Detection of Embryonic Neurodevelopmental Disorders. Diagnostics, 10.","DOI":"10.3390\/diagnostics10010027"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Pan, H., Wang, X., and Lin, Z. (2020). Machine Learning-Enriched Lamb Wave Approaches for Automated Damage Detection. Sensors, 20.","DOI":"10.3390\/s20061790"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","article-title":"Physiological time series analysis using approximate entropy and sample entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. J. Physiol. Heart Circ."},{"key":"ref_32","unstructured":"Shannon, C.E., and Weaver, W. (1964). The Mathematical Theory of Communication, University of Illinois Press."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1109\/18.119732","article-title":"Entropy-based algorithms for best basis selection","volume":"38","author":"Coifman","year":"1992","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.artmed.2009.03.003","article-title":"Entropy and complexity measures for EEG signal classification of schizophrenic and control participants","volume":"47","author":"Sabeti","year":"2009","journal-title":"Artif. Intell. Med."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2626","DOI":"10.1007\/s10439-009-9795-x","article-title":"Log energy entropy-based EEG classification with multilayer neural networks in seizure","volume":"37","author":"Saraoglu","year":"2009","journal-title":"Ann. Biomed. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"6295","DOI":"10.1016\/j.eswa.2008.07.012","article-title":"An expert system for speaker identification using adaptive wavelet sure entropy","volume":"36","author":"Avci","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/S0010-4825(03)00002-7","article-title":"An Intelligent system for diagnosis of the heart valve diseases with wavelet packet natural Networks","volume":"33","author":"Turkoglu","year":"2003","journal-title":"Comput. Biol. Med."},{"key":"ref_38","unstructured":"Duda, R., Hart, P., and Stork, D. (2006). Pattern Classification, John Wiley and Sons. [2nd ed.]."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ranganathan, G., Fernando, X., and Shi, F. (2022). Recognition of Facial Expression Using Haar Cascade Classifier and Deep Learning. Inventive Communication and Computational Technologies, Springer. Lecture Notes in Networks and Systems.","DOI":"10.1007\/978-981-16-5529-6"},{"key":"ref_40","unstructured":"Codella, N., Rotemberg, V., Tschandl, P., Celebi, M.E., Dusza, S., Gutman, D., Helba, B., Kalloo, A., Liopyris, K., and Marchetti, M. (2019). Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (ISIC). arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Mustafa, S., Dauda, A.B., and Dauda, M. (2017, January 29\u201331). Image processing and SVM classification for melanoma detection. Proceedings of the 2017 International Conference on Computing Networking and Informatics (ICCNI), Ota, Nigeria.","DOI":"10.1109\/ICCNI.2017.8123777"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kaur, R., GholamHosseini, H., Sinha, R., and Lind\u00e9n, M. (2022). Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images. Sensors, 22.","DOI":"10.1186\/s12880-022-00829-y"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"101843","DOI":"10.1016\/j.compmedimag.2020.101843","article-title":"Automated multi-class classification of skin lesions through deep convolutional neural network with dermoscopic images","volume":"88","author":"Iqbal","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"29353","DOI":"10.1007\/s11042-020-09431-2","article-title":"Interpreting SVM for medical images using Quadtree","volume":"79","author":"Shukla","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"92809","DOI":"10.1109\/ACCESS.2022.3202651","article-title":"Levelized Multiple Workflow Allocation Strategy under Precedence Constraints with Task Merging in IaaS Cloud Environment","volume":"10","author":"Ahmad","year":"2022","journal-title":"IEEE Access"},{"key":"ref_46","first-page":"1","article-title":"A Comparative Study of Fuzzy Optimization through Fuzzy Number","volume":"1","author":"Kumar","year":"2021","journal-title":"Int. J. Mod. Res."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hosny, K.M., Kassem, M.A., and Foaud, M.M. (2019). Classification of skin lesions using transfer learning and augmentation with Alex-net. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0217293"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/12\/583\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:42:10Z","timestamp":1760146930000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/12\/583"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,15]]},"references-count":47,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["info13120583"],"URL":"https:\/\/doi.org\/10.3390\/info13120583","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,15]]}}}