Enhancing DeepMelaNet Accuracy Through Hairline Removal and Image Contrast Enhancement

Abstract

Melanoma is one of the most dangerous types of skin cancer, originating from melanocytes that produce melanin as skin pigment. This disease can develop aggressively and has a high risk of spreading to other organs if not detected early. Accurate diagnosis makes it possible to prevent this disease and avoid death from melanoma. This study aims to optimize image-based melanoma classification results by utilizing the DeepMelaNet model through the integration of two main preprocessing techniques, namely hair line removal and contrast enhancement using the CLAHE method. The problem of inconsistent dermoscopy image quality is an obstacle in identifying relevant clinical details, so preprocessing techniques are needed to improve the generalization and reliability of the model. This study also applies several augmentations which are then further processed with DeepMelaNet-based classification experiments. Evaluations are carried out on various preprocessing combination scenarios to assess the impact on classification accuracy. The main findings show that the sequence of applying contrast enhancement followed by hairline removal and optimal learning rate adjustment can improve validation accuracy to 96.5 percent, surpassing the standalone DeepMelaNet approach and other combinations while producing the most stable training performance. This study confirms that the selection and adjustment of preprocessing strategies play a crucial role in improving the accuracy and generalization of deep learning models for melanoma classification tasks, so that this method can be recommended as a standard in Deep Learning-based dermoscopy medical image analysis.