{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T12:41:28Z","timestamp":1774442488814,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,1,16]],"date-time":"2018-01-16T00:00:00Z","timestamp":1516060800000},"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>Identification of the malignancy of tissues from Histopathological images has always been an issue of concern to doctors and radiologists. This task is time-consuming, tedious and moreover very challenging. Success in finding malignancy from Histopathological images primarily depends on long-term experience, though sometimes experts disagree on their decisions. However, Computer Aided Diagnosis (CAD) techniques help the radiologist to give a second opinion that can increase the reliability of the radiologist\u2019s decision. Among the different image analysis techniques, classification of the images has always been a challenging task. Due to the intense complexity of biomedical images, it is always very challenging to provide a reliable decision about an image. The state-of-the-art Convolutional Neural Network (CNN) technique has had great success in natural image classification. Utilizing advanced engineering techniques along with the CNN, in this paper, we have classified a set of Histopathological Breast-Cancer (BC) images utilizing a state-of-the-art CNN model containing a residual block. Conventional CNN operation takes raw images as input and extracts the global features; however, the object oriented local features also contain significant information\u2014for example, the Local Binary Pattern (LBP) represents the effective textural information, Histogram represent the pixel strength distribution, Contourlet Transform (CT) gives much detailed information about the smoothness about the edges, and Discrete Fourier Transform (DFT) derives frequency-domain information from the image. Utilizing these advantages, along with our proposed novel CNN model, we have examined the performance of the novel CNN model as Histopathological image classifier. To do so, we have introduced five cases: (a) Convolutional Neural Network Raw Image (CNN-I); (b) Convolutional Neural Network CT Histogram (CNN-CH); (c) Convolutional Neural Network CT LBP (CNN-CL); (d) Convolutional Neural Network Discrete Fourier Transform (CNN-DF); (e) Convolutional Neural Network Discrete Cosine Transform (CNN-DC). We have performed our experiments on the BreakHis image dataset. The best performance is achieved when we utilize the CNN-CH model on a 200\u00d7 dataset that provides Accuracy, Sensitivity, False Positive Rate, False Negative Rate, Recall Value, Precision and F-measure of 92.19%, 94.94%, 5.07%, 1.70%, 98.20%, 98.00% and 98.00%, respectively.<\/jats:p>","DOI":"10.3390\/info9010019","type":"journal-article","created":{"date-parts":[[2018,1,17]],"date-time":"2018-01-17T04:23:44Z","timestamp":1516163024000},"page":"19","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":112,"title":["Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2391-5767","authenticated-orcid":false,"given":"Abdullah-Al","family":"Nahid","sequence":"first","affiliation":[{"name":"School of Engineering, Macquarie University, Sydney, NSW 2109, Australia"}]},{"given":"Yinan","family":"Kong","sequence":"additional","affiliation":[{"name":"School of Engineering, Macquarie University, Sydney, NSW 2109, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1651","DOI":"10.1088\/0031-9155\/46\/6\/305","article-title":"An SVM classifier to separate false signals from microcalcifications in digital mammograms","volume":"46","author":"Bazzani","year":"2002","journal-title":"Phys. Med. Biol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1552","DOI":"10.1109\/TMI.2002.806569","article-title":"A support vector machine approach for detection of microcalcifications","volume":"21","author":"Yang","year":"2002","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mhala, N.C., and Bhandari, S.H. (2016, January 6\u20138). Improved approach towards classification of histopathology images using bag-of-features. Proceedings of the 2016 International Conference on Signal and Information Processing (IConSIP), Vishnupuri, India.","DOI":"10.1109\/ICONSIP.2016.7857472"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Dheeba, J., and Selvi, S.T. (2011, January 23\u201324). Classification of malignant and benign microcalcification using svm classifier. Proceedings of the 2011 International Conference on Emerging Trends in Electrical and Computer Technology, Nagercoil, India.","DOI":"10.1109\/ICETECT.2011.5760205"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Taheri, M., Hamer, G., Son, S.H., and Shin, S.Y. (2016, January 11\u201314). Enhanced breast cancer classification with automatic thresholding using svm and harris corner detection. Proceedings of the International Conference on Research in Adaptive and Convergent Systems (RACS\u2018 16), Odense, Denmark.","DOI":"10.1145\/2987386.2987420"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Shirazi, F., and Rashedi, E. (2016, January 9\u201311). Detection of cancer tumors in mammography images using support vector machine and mixed gravitational search algorithm. Proceedings of the 2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC), Bam, Iran.","DOI":"10.1109\/CSIEC.2016.7482133"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1109\/TMI.2008.916959","article-title":"Classification of dynamic contrast-enhanced magnetic resonance breast lesions by support vector machines","volume":"27","author":"Levman","year":"2008","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Angayarkanni, S.P., and Kamal, N.B. (2012, January 3\u20134). Mri mammogram image classification using id3 algorithm. Proceedings of the IET Conference on Image Processing (IPR 2012), London, UK.","DOI":"10.1049\/cp.2012.0464"},{"key":"ref_9","first-page":"258","article-title":"Evaluating diagnostic performance of machine learning algorithms on breast cancer","volume":"Volume 9243","author":"Gatuha","year":"2015","journal-title":"Revised Selected Papers, Part II, Proceedings of the 5th International Conference on Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques (IScIDE), Suzhou, China, 14\u201316 June 2015"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1063\/1.3596623","article-title":"Breast cancer classification from histological images with multiple features and random subspace classifier ensemble","volume":"1371","author":"Zhang","year":"2011","journal-title":"AIP Conf. Proc."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Diz, J., Marreiros, G., and Freitas, A. (2015). Using Data Mining Techniques to Support Breast Cancer Diagnosis, Springer International Publishing.","DOI":"10.1007\/978-3-319-16486-1_68"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kendall, E.J., and Flynn, M.T. (2014). Automated Breast Image Classification Using Features from Its Discrete Cosine Transform. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0091015"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Burling-Claridge, F., Iqbal, M., and Zhang, M. (2016, January 24\u201329). Evolutionary algorithms for classification of mammographie densities using local binary patterns and statistical features. Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, Canada.","DOI":"10.1109\/CEC.2016.7744277"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Rajakeerthana, K.T., Velayutham, C., and Thangavel, K. (2014). Mammogram Image Classification Using Rough Neural Network. Computational Intelligence, Cyber Security and Computational Models, Springer India.","DOI":"10.1007\/978-81-322-1680-3_15"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lessa, V., and Marengoni, M. (2016). Applying Artificial Neural Network for the Classification of Breast Cancer Using Infrared Thermographic Images. Computer Vision and Graphics, Proceedings of the International Conference on Computer Vision and Graphics (ICCVG 2016), Warsaw, Poland, 19\u201321 September 2016, Springer International Publishing.","DOI":"10.1007\/978-3-319-46418-3_38"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.cmpb.2015.09.019","article-title":"An automated confirmatory system for analysis of mammograms","volume":"125","author":"Peng","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_17","unstructured":"Silva, S., Costa, M., Pereira, W., and Filho, C. (2015, January 25\u201329). Breast tumor classification in ultrasound images using neural networks with improved generalization methods. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lopez-Melendez, E., Lara-Rodriguez, L.D., Lopez-Olazagasti, E., Sanchez-Rinza, B., and Tepichin-Rodriguez, E. (2012, January 27\u201329). Bicad: Breast image computer aided diagnosis for standard birads 1 and 2 in calcifications. Proceedings of the 22nd International Conference on Electrical Communications and Computers (CONIELECOMP 2012), Cholula, Puebla, Mexico.","DOI":"10.1109\/CONIELECOMP.2012.6189907"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF00344251","article-title":"Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position","volume":"36","author":"Fukushima","year":"1980","journal-title":"Biol. Cybern."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wu, C.Y., Lo, S.C.B., Freedman, M.T., Hasegawa, A., Zuurbier, R.A., and Mun, S.K. (1994). Classification of microcalcifications in radiographs of pathological specimen for the diagnosis of breast cancer. Medical Imaging 1994: Image Processing, International Society for Optics and Photonics.","DOI":"10.1117\/12.175099"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.cmpb.2015.12.014","article-title":"Representation learning for mammography mass lesion classification with convolutional neural networks","volume":"127","author":"Arevalo","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"012060","DOI":"10.1088\/1742-6596\/783\/1\/012060","article-title":"Classification of breast cancer cytological specimen using convolutional neural network","volume":"783","author":"Zejmo","year":"2017","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_23","unstructured":"Qiu, Y., Wang, Y., Yan, S., Tan, M., Cheng, S., Liu, H., and Zheng, B. (March, January 27). An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology. Proceedings of the SPIE Medical Imaging, San Diego, CA, USA."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jiang, F., Liu, H., Yu, S., and Xie, Y. (2017, January 6\u20138). Breast mass lesion classification in mammograms by transfer learning. Proceedings of the 5th International Conference on Bioinformatics and Computational Biology (ICBCB\u201817), Hong Kong, China.","DOI":"10.1145\/3035012.3035022"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Suzuki, S., Zhang, X., Homma, N., Ichiji, K., Sugita, N., Kawasumi, Y., Ishibashi, T., and Yoshizawa, M. (2016, January 20\u201323). Mass detection using deep convolutional neural network for mammographic computer-aided diagnosis. Proceedings of the 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Tsukuba, Japan.","DOI":"10.1109\/SICE.2016.7749265"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"044501","DOI":"10.1117\/1.JMI.3.4.044501","article-title":"Microscopic medical image classification framework via deep learning and shearlet transform","volume":"3","author":"Rezaeilouyeh","year":"2016","journal-title":"J. Med. Imaging"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sharma, K., and Preet, B. (2016, January 21\u201324). Classification of mammogram images by using cnn classifier. Proceedings of the 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India.","DOI":"10.1109\/ICACCI.2016.7732477"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.neucom.2016.02.060","article-title":"A deep feature based framework for breast masses classification","volume":"197","author":"Jiao","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.media.2016.07.007","article-title":"Large scale deep learning for computer aided detection of mammographic lesions","volume":"35","author":"Kooi","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Anand, S., and Rathna, R.A.V. (2013, January 25\u201326). Detection of architectural distortion in mammogram images using contourlet transform. Proceedings of the 2013 IEEE International Conference on Emerging Trends in Computing, Communication and Nanotechnology (ICECCN), Tirunelveli, India.","DOI":"10.1109\/ICE-CCN.2013.6528488"},{"key":"ref_31","unstructured":"Moayedi, F., Azimifar, Z., Boostani, R., and Katebi, S. (2007). Contourlet-Based Mammography Mass Classification, Springer."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1016\/j.proeng.2012.06.077","article-title":"Nonsubsampled contourlet transform based classification of microcalcification in digital mammograms","volume":"38","author":"Jasmine","year":"2012","journal-title":"Proc. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.cmpb.2015.06.009","article-title":"Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution","volume":"122","author":"Pak","year":"2015","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2091","DOI":"10.1109\/TIP.2005.859376","article-title":"The contourlet transform: An efficient directional multiresolution image representation","volume":"14","author":"Do","year":"2005","journal-title":"IEEE Trans. Image Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1109\/TCOM.1983.1095851","article-title":"The laplacian pyramid as a compact image code","volume":"31","author":"Burt","year":"1983","journal-title":"IEEE Trans. Commun."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","article-title":"Multiresolution gray-scale and rotation invariant texture classification with local binary patterns","volume":"24","author":"Ojala","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1137\/S0036144598336745","article-title":"The discrete cosine transform","volume":"41","author":"Strang","year":"1999","journal-title":"SIAM Rev."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Marom, N.D., Rokach, L., and Shmilovici, A. (2010, January 17\u201320). Using the confusion matrix for improving ensemble classifiers. Proceedings of the 2010 IEEE 26-th Convention of Electrical and Electronics Engineers in Israel, Eliat, Israel.","DOI":"10.1109\/EEEI.2010.5662159"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.1109\/TBME.2015.2496264","article-title":"A dataset for breast cancer histopathological image classification","volume":"63","author":"Spanhol","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_40","unstructured":"Brook, E., El-yaniv, R., Isler, E., Kimmel, R., Member, S., Meir, R., and Peleg, D. (2006). Breast cancer diagnosis from biopsy images using generic features and svms. IEEE Trans. Biomed. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, B. (2011, January 15\u201317). Breast cancer diagnosis from biopsy images by serial fusion of random subspace ensembles. Proceedings of the 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI), Shanghai, China.","DOI":"10.1109\/BMEI.2011.6098229"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Cire\u015fan, D.C., Giusti, A., Gambardella, L.M., and Schmidhuber, J. (2013). Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks, Springer.","DOI":"10.1007\/978-3-642-40763-5_51"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"034003","DOI":"10.1117\/1.JMI.1.3.034003","article-title":"Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features","volume":"1","author":"Wang","year":"2014","journal-title":"J. Med. Imaging"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Spanhol, F.A., Oliveira, L.S., Petitjean, C., and Heutte, L. (2016, January 24\u201329). Breast cancer histopathological image classification using convolutional neural networks. Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada.","DOI":"10.1109\/IJCNN.2016.7727519"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4172","DOI":"10.1038\/s41598-017-04075-z","article-title":"Breast cancer multi-classification from histopathological images with structured deep learning model","volume":"7","author":"Han","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Dimitropoulos, K., Barmpoutis, P., Zioga, C., Kamas, A., Patsiaoura, K., and Grammalidis, N. (2017). Grading of invasive breast carcinoma through grassmannian vlad encoding. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0185110"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.sigpro.2015.11.011","article-title":"Automatic cell nuclei segmentation and classification of breast cancer histopathology images","volume":"122","author":"Wang","year":"2016","journal-title":"Signal Process."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/9\/1\/19\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:51:27Z","timestamp":1760194287000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/9\/1\/19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,16]]},"references-count":47,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2018,1]]}},"alternative-id":["info9010019"],"URL":"https:\/\/doi.org\/10.3390\/info9010019","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1,16]]}}}