Towards Enhanced Deep CNN For Early And Precise Skin Cancer Diagnosis

Abstract

Most people’s first experience with cancer will be with skin cancer, which is also the most prevalent and potentially fatal kind. Determining a skin cancer diagnosis also requires the use of information technologies. This highlights the need of developing and deploying highly effective deep-learning methods for the early and accurate diagnosis and detection of skin cancer. Deep Convolution Neural Network (DCNN) is proposed for automated skin cancer detection in this study. This study’s unique contribution is the use of a deep convolution neural network containing 12 nested processing layers to improve the accuracy of skin cancer diagnosis and detection. As a consequence of this study’s findings, researchers have determined that deep learning techniques are superior to machine learning for spotting skin cancer. As a consequence, pathologists’ precision and competence may be improved by using automated evidence-based detection of skin cancer. To accurately distinguish between benign and malignant skin lesions, we present a deep convolution neural network (DCNN) model in this research that uses a deep learning technique. First, we normalize the input photos and identify characteristics that aid in correct classification, then we apply a filter or Gaussian to eliminate noise and artifacts, and lastly, we supplement the data to increase the number of images, which enhances the accuracy of the classification rate.