Skin cancer classification using Inception Network

Since skin disease is a universally recognized condition among humans, there has been a growing interest in utilizing intelligent systems to classify various skin ailments. This line of research in deep learning holds immense significance for dermatologists. However, accurately determining the presence of a disease is a formidable task due to the intricate nature of skin texture and the visual similarities between different diseases. To address this challenge, skin images undergo filtration to eliminate unwanted noise and undergo further processing to enhance the overall quality of the image. The primary purpose of this study is to construct a deep neural network-based model that is capable of automatically classifying several types of skin cancer as either melanoma or non-melanoma with a prominent level of accuracy. We propose an optimized Inception architecture, in which the InceptionNet model is enhanced with data augmentation and basic layers. The strategy that has been proposed enhances the model's capacity to deal with incomplete and inconsistent data. A dataset of 2637 skin images are used to demonstrate the benefits of the technique that has been proposed. We analyze the performance of the suggested method by looking at its precision, sensitivity, specificity, F1-score, and area under the ROC curve. Proposed InceptionNet provides an accuracy of 84.39% and 85.94%, respectively for Adam and Nadam optimizer. The training process in each subsequent layer exhibits a notable enhancement in effectiveness. An examination of this inquiry can assist experts in making early diagnoses, thereby providing them with insight into the nature of the infection and enabling them to initiate the necessary treatment, if deemed necessary.