hybSVM: Bacterial colony optimization algorithm based SVM for malignant melanoma detection

Abstract Melanoma is a malignant and aggressive type of skin cancer. This paper describes an effective method for detection of melanoma. A hybrid classification algorithm was developed by using the SVM algorithm and a heuristic optimization algorithm. In this algorithm, the SVM algorithm which uses a Gaussian Radial Basis Function (RBF) was enhanced by the Bacterial Colony algorithm (hybSVM). The model was tested with two different datasets namely ISIC and PH2 by using 10 cross fold validation. According to results AUC value of 98%, 97% and an operation time of 26.5, 11.9 sec obtained respectively from ISIC and PH2.

[2]  Sebastiano Battiato,et al.  Advanced skin lesion discrimination pipeline for early melanoma cancer diagnosis towards PoC devices , 2017, 2017 European Conference on Circuit Theory and Design (ECCTD).

[3]  Maria L. Wei,et al.  The role of technology in melanoma screening and diagnosis , 2020, Pigment cell & melanoma research.

[4]  Li Zhang,et al.  An intelligent decision support system for skin cancer detection from dermoscopic images , 2016, 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD).

[5]  Marcin Wozniak,et al.  An Intelligent System for Monitoring Skin Diseases , 2018, Sensors.

[6]  Yading Yuan,et al.  Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance , 2017, IEEE Transactions on Medical Imaging.

[7]  K. P. Soman,et al.  Deep power: Deep learning architectures for power quality disturbances classification , 2017, 2017 International Conference on Technological Advancements in Power and Energy ( TAP Energy).

[8]  A. Ben Hamza,et al.  Melanoma detection using adversarial training and deep transfer learning , 2020, Physics in medicine and biology.

[9]  J. Deeks,et al.  Sensitivity and specificity of SkinVision are likely to have been overestimated , 2020, Journal of the European Academy of Dermatology and Venereology : JEADV.

[10]  Bingsheng He,et al.  ThunderSVM: A Fast SVM Library on GPUs and CPUs , 2018, J. Mach. Learn. Res..

[11]  E. Carrera,et al.  A Computer Aided Diagnosis System for Skin Cancer Detection , 2018, Communications in Computer and Information Science.

[12]  Hayri Sever,et al.  Skor Dağılımlı Üst Arama Modeli , 2006 .

[13]  Dawid Połap,et al.  Intelligent Home Systems for Ubiquitous User Support by Using Neural Networks and Rule-Based Approach , 2020, IEEE Transactions on Industrial Informatics.

[14]  Utku Köse.  Zeki Optimizasyon Tabanlı Destek Vektör Makineleri ile Diyabet Teşhisi , 2018 .

[15]  Enes Ayan,et al.  Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm , 2019, Diagnostics.

[16]  André Carlos Ponce de Leon Ferreira de Carvalho,et al.  Combining meta-learning and search techniques to select parameters for support vector machines , 2012, Neurocomputing.

[17]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  K. P. Sanal Kumar,et al.  Detection of Skin Cancer Using SVM, Random Forest and kNN Classifiers , 2019, Journal of Medical Systems.

[19]  T. Subashini,et al.  Detection and diagnosis of dilated cardiomyopathy and hypertrophic cardiomyopathy using image processing techniques , 2016 .

[20]  M. M. Saritas,et al.  Performance Analysis of ANN and Naive Bayes Classification Algorithm for Data Classification , 2019 .

[21]  Van-Dung Hoang,et al.  Deep CNN and Data Augmentation for Skin Lesion Classification , 2018, ACIIDS.

[22]  A. Kalloo,et al.  Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images , 2018, Journal of the American Academy of Dermatology.

[23]  Marcelo A C Fernandes,et al.  Proposal of the CAD System for Melanoma Detection Using Reconfigurable Computing , 2020, Sensors.

[24]  P. D. Sathya,et al.  Trading strategies for image segmentation using multilevel thresholding aided with minimum cross entropy , 2020 .

[25]  Akio Kimura,et al.  A SVM-based diagnosis of melanoma using only useful image features , 2018, 2018 International Workshop on Advanced Image Technology (IWAIT).

[26]  E. Dinçer,et al.  Gırtlak Kanseri Ameliyat Verilerinin K-means Yöntemiyle Analizi , 2006 .

[27]  Zainul Abdin Jaffery,et al.  Bag of feature and support vector machine based early diagnosis of skin cancer , 2020, Neural Computing and Applications.

[28]  Hao Chen,et al.  Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks , 2017, IEEE Transactions on Medical Imaging.

[29]  Hanan A. R. Akkar,et al.  Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent , 2020, Engineering and Technology Journal.

[30]  Z. Aydın.  Performance Analysis of Machine Learning and Bioinformatics Applications on High Performance Computing Systems , 2020 .

[31]  Khalid M. Hosny,et al.  Classification of skin lesions using transfer learning and augmentation with Alex-net , 2019, PloS one.

[32]  Amna Waheed,et al.  An efficient machine learning approach for the detection of melanoma using dermoscopic images , 2017, 2017 International Conference on Communication, Computing and Digital Systems (C-CODE).

[33]  Eugenio Vocaturo,et al.  Melanoma Detection by Means of Multiple Instance Learning , 2019, Interdisciplinary Sciences: Computational Life Sciences.

[34]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[35]  Huan Zhang,et al.  Class-specific attribute value weighting for Naive Bayes , 2020, Inf. Sci..

[36]  Chandan Chakraborty,et al.  Automated Tissue Classification Framework for Reproducible Chronic Wound Assessment , 2014, BioMed research international.

[37]  Marcin Wozniak,et al.  Bio-inspired methods modeled for respiratory disease detection from medical images , 2018, Swarm Evol. Comput..

[38]  LinLin Shen,et al.  Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network , 2017, Sensors.

[39]  Harald Ganster,et al.  Automated Melanoma Recognition , 2001, IEEE Trans. Medical Imaging.

[40]  Amani Alamri,et al.  On the development of a skin cancer computer aided diagnosis system using support vector machine , 2019, Bioscience Biotechnology Research Communications.

[41]  Pedro Pedrosa Rebouças Filho,et al.  Automatic skin lesions segmentation based on a new morphological approach via geodesic active contour , 2019, Cognitive Systems Research.

[42]  Bengisu Tulu,et al.  Area Determination of Diabetic Foot Ulcer Images Using a Cascaded Two-Stage SVM-Based Classification , 2017, IEEE Transactions on Biomedical Engineering.

[43]  Marta Canuti,et al.  Influenza and Other Respiratory Viruses Involved in Severe Acute Respiratory Disease in Northern Italy during the Pandemic and Postpandemic Period (2009–2011) , 2014, BioMed research international.

[44]  Metin Nafi Gürcan,et al.  Evolving strategies for the development and evaluation of a computerised melanoma image analysis system , 2018, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

[45]  Madjid Fathi,et al.  DePicT Melanoma Deep-CLASS: a deep convolutional neural networks approach to classify skin lesion images , 2020, BMC Bioinformatics.

[46]  Nazia Hameed,et al.  A comprehensive survey on image-based computer aided diagnosis systems for skin cancer , 2016, 2016 10th International Conference on Software, Knowledge, Information Management & Applications (SKIMA).

[47]  Maciej Ogorzalek,et al.  Resolution invariant wavelet features of melanoma studied by SVM classifiers , 2019, PloS one.

[48]  D SeejaR,et al.  Deep Learning Based Skin Lesion Segmentation and Classification of Melanoma Using Support Vector Machine (SVM) , 2019 .

[49]  Mun-Taek Choi,et al.  Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks , 2018, Comput. Methods Programs Biomed..

[50]  Hong Wang,et al.  Bacterial Colony Optimization , 2012 .

[51]  A.S.Z. Belloum,et al.  A Survey of Scalable Deep Learning Frameworks , 2019, 2019 15th International Conference on eScience (eScience).

[52]  Manoj Kumar,et al.  A DE-ANN Inspired Skin Cancer Detection Approach Using Fuzzy C-Means Clustering , 2020, Mob. Networks Appl..

[53]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[54]  D. Pham,et al.  Selection of K in K-means clustering , 2005 .