An Integrative Health Care Informatics Model for Early Detection of Skin Cancer

Because lesion features and detection backgrounds are complex, automatic lesion detection in dermoscopy images is fraught with difficulties. Using more significant and more complicated models has been the primary strategy used by previous methods to improve detection accuracy. However, these methods frequently miss significant variations within classes and commonalities between classes in lesion characteristics. This research gap restricts our comprehension of the subtle differences within lesion classes and the commonalities among several classes. The use of bigger model sizes further complicates implementing algorithms in real-world contexts. Consequently, studies that delve further into the underlying intricacies of lesion features and investigate novel ways to overcome these obstacles while guaranteeing the scalability and applicability of the detection algorithms are desperately needed. This research aims to tackle the issue of insufficient annotated data in skin cancer diagnosis by proposing a new and innovative 3D neural network (NN) model based on deep learning techniques. The design of our model is tailored to specifically detect several types of skin cancer, such as Melanoma, Nevus, Actinic keratosis, and Dermatofibroma. To address the limitations of the available data, we utilise an augmentation technique to increase the size of the dataset. This helps to improve the model's ability to handle different scenarios and make accurate predictions while avoiding overfitting. By doing thorough experiments, we have achieved an impressive accuracy rate of 93.30% in distinguishing Actinic keratosis from Nevus. This demonstrates the efficacy of our suggested method in properly recognising various forms of skin lesions.