Enhancement of Multiclass Dermoscopic Image Classification with a Fusion Model and Fine-Tuned Deep Neural Networks

Abstract

Since malignancy of the skin is a frequent and sometimes fatal illness, prompt and precise detection is essential to successful treatment. Dermoscopic imaging is now emerging as a helpful tool for dermatologists in aiding with the identification of various types of skin lesions and offering timely treatments. Due to varied image conditions, data unbalancing, and fine-grained pattern classification task are is challenging in skin cancer detection. Dermoscopic images vary greatly in quality, illumination, and orientation, making it challenging for models to efficiently generalize across varied image conditions. To obtain accurate and reliable skin cancer detection, such challenges require the utilization of sophisticated deep learning techniques and rigorous model development. In multiple classes skin malignancy classification, convolutional neural networks outperform dermatology professionals, according to recent research. In the proposed system, firstly a preprocessing technique has been developed that includes hair and noise removal, data augmentation, and image resizing with model parameters. Then training has been performed using transfer learning on the fusion of DenseNet121 and ResNet50V2 model on the HAM10000 dataset. The proposed technique's performance is assessed using measures such as sensitivity, f-1 score, specificity, and accuracy. For multi-class skin lesion classification, the suggested technique achieves a precision of 97.88%, F-Score of 98%, sensitivity of 99%, accuracy of 97.08%, and Mathew's correlation coefficient (MCC) of 96.4%, it executes superior to cutting-edge techniques.