Skin cancer detection using dermoscopic images with convolutional neural network

Skin malignant melanoma is a high-risk tumor with low incidence but high mortality rates. Early detection and treatment are crucial for a cure. Machine learning studies have focused on classifying melanoma tumors, but these methods are cumbersome and fail to extract deeper features. This limits their ability to distinguish subtle variations in skin lesions accurately, hindering effective early diagnosis. The study introduces a deep learning-based network specifically designed for skin lesion detection to enhance data in the melanoma dataset. It leverages a novel FCDS-CNN architecture to address class-imbalanced problems and improve data quality. Specifically, FCDS-CNN incorporates data augmentation and class weighting techniques to mitigate the impact of imbalanced classes. It also presents a practical, large-scale solution that allows seamless, real-world incorporation to support dermatologists in their early screening processes. The proposed robust model incorporates data augmentation and class weighting to improve performance across all lesions. The proposed dataset includes 10015 images of seven classes of skin lesions available in Kaggle. To overcome the dominance of one class over the other, methods like data augmentation and class weighting are used. The FCDS-CNN showed improved accuracy with an average accuracy of 96%, outperforming pre-trained models such as ResNet, EfficientNet, Inception, and MobileNet in the precision, recall, F1-score, and area under the curve parameters. These pre-trained models are more effective for general image classification and struggle with the nuanced features and class imbalances inherent in medical image datasets. The FCDS-CNN demonstrated practical effectiveness by outperforming the compared pre-trained model based on distinct parameters. This work is a testament to the importance of specificity in medical image analysis regarding skin cancer detection.