Deep Learning Techniques to Detect Skin Cancer

Abstract

Deep Learning has been generally used in the last few years to increase diagnosis accuracy in medical imaging tasks including classification and segmentation. Advanced deep learning algorithms use layers of neural networks to extract key visual properties, essential image features, making the learning process increasingly difficult as more layers are added. Comparing deep learning to traditional machine learning methods reveals a number of advantages, but it is dependent on huge, annotated datasets, which are frequently constrained by publicly accessible resources. This thesis suggests a practical method for overcoming this data shortage that improves the precision of skin lesion diagnosis, particularly for the classification of melanoma, by combining a collection of convolutional neural networks (CNNs) with generative adversarial network (GAN) based augmentation. With a small dataset, the first suggested model performed with an accuracy of 93.36%. The second suggested method used conventional augmentation to increase the dataset, and it provided superior data validation and accuracy of 60.16%. The training dataset was increased using the final proposed method using both conventional and GAN based augmentation techniques. With greater data validation, it performed better, with a performance accuracy of 95.01%. Additionally, it avoided trapping into local maxima. Which improved performance. When dealing with the little training dataset, the scheme using the methods could be a preferable