A Deep Neural Network Solution For Malignant Melanoma Detection
Volume 3 - Issue 11, November 2019 Edition
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Bishwa Raj Poudel, Bibash Adhikari, Amrit Koirala, Sunil Dahal
Computer Aided Detection and Diagnosis, Machine Learning, Neural nets, Pattern Recognition, Segmentation.
This paper is an approach to predict the probability of an infected dermatoscopic image being cancerous (malignant melanoma) or not (benign) using learning power of Layered Convolutional Neural Network. Since the infected part of malignant highly resembles benign images, melanoma being the deadly fatal disease and confusion of dermatologists on differentiating malignant from benign easily has made this research a perfect application of machine learning to ease the field of Medical Science. Also, this research aims to propose computerized detection of malignant melanoma thereby preventing costly biopsy procedures that is otherwise done in clinic for diagnosing the disease. For this research, multiple architectures of layered neural network are designed, implemented and training is done by feeding those networks to around 13K dermatoscopic images using different training algorithms, multiple filters and tuning hyper-parameters in those algorithms to get the optimum result. 3 of the best performing milestone architectures with their hyper-parameter values and result analysis of those architectures is reflected in this paper. The best result we could achieve was with transfer learning using Google’s Inception-v3 which resulted out the staggering 94.02% of training accuracy and 89.73% of validation accuracy with f1-score of 0.81.
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