Enhanced AlexNet Model

• Tech Stack:Python, google colab notebook, MATLAB, Tensorflow, Keras 3 API
• Research Paper: Link
• Github URL: Project Link

Melanoma, a potentially lethal form of Skin Cancer(SC), poses a diagnostic challenge due to its visual similarities with nevus, making accurate differentiation difficult. The mortality rate associated with melanoma surpasses that of other consolidated skin malignancies, and its incidence is on the rise, particularly among young individuals. Timely diagnosis at an early stage significantly improves survival rates. However, the current diagnostic process demands substantial time and resources. In this study, we present a groundbreaking intelligent system leveraging advanced deep learning techniques to detect melanoma . Our approach utilizes pre-existing deep neural networks, namely the AlexNet. Here, we retained the fundamental model structure while substituting the border layers with Dense layers. The modified AlexNet with 3 dense layer provides 100 % classification accuracy as compare to other existing models.

In this work, we have used the Deep Learning (DL) method in order that we can detect melanoma SC very easily. First, we used the augmentation technique as our dataset has less number of images. So that we can increase our dataset. Then we split the augmentation data, which is divided into train and validation. Then we used CNN that is Alexnet and we got 98.65% accuracy