Automatic melanoma detection using an optimized five-stream convolutional neural network

Melanoma is among the deadliest forms of malignant skin cancer, with the number of cases increasing dramatically worldwide. Its early and accurate diagnosis is crucial for effective treatment. However, automatic melanoma detection has several significant challenges. These challenges include the lack of a balanced dataset, high variability within melanoma lesions, differences in the locations of skin lesions in images, the similarity between different skin lesions, and the presence of various artifacts. In addition, the previous deep-learning techniques for diagnosing melanoma cannot recognize the unique relations between samples. For this reason, these convolutional neural networks (CNNs) cannot perceive the changed or rotated image samples as similar. To address these issues in this paper, we have done pre-processing such as hair removal, balancing the skin lesion images using a generative adversarial network (GAN)-based method, denoising using a CNN-based method, and image enhancement. In addition, we propose four new methods to extract key features: the hybrid ULBP and Chan-Vese algorithm (ULBP-CVA), multi-block ULBP on the nine suggested planes (multi-block ULBP-NP), a combination of multi-block Gabor magnitude and phase with ULBP-NP (multi-block GULBP-NP), and combining multi-block gradient magnitude and orientation with ULBP-NP (multi-block gradient ULBP-NP). We suggest nine planes to grab the most vital information about skin lesions in any direction for accurate coding. These introduced planes can capture synchronous spatial and local variations. Hence, very similar lesions can be differentiated by revealing small changes in these small planes. Finally, we propose an optimized four-stream CNN (OFSCNN) for classification. It can simultaneously classify the lesion color, lesion edges, texture features, local-spatial frequency features, and multi-oriented gradient features. The simulation results of our proposed method are promising compared to the most relevant state-of-the-art methods for melanoma detection in dermoscopy images. Our proposed method has automatically detected melanoma in 99.8%, 99.9%, 99.62%, and 99.6% of the HAM 10000, ISIC 2024, ISIC 2017, and ISIC 2016 datasets, respectively.