Enhanced melanoma detection using a fuzzy ensemble approach integrating hybrid optimization algorithm

Abstract

The high death rate of melanoma makes it an important public health issue all over the world. Therefore, early detection of melanoma is crucial for a good prognosis. Deriving meaningful features from dermoscopic pictures, however, is difficult for a number of reasons, including a lack of training data, inconsistent classes, and intra-class variability. In order to solve this issue, we offer an automated technique for melanoma diagnosis from dermoscopic pictures by using high-level characteristics obtained from a powerful CNN architecture and LightGBM. The Ant Lion Optimization may get fixated on a local optimum rather than a global optimum as the problem's complexity rises. This may be a stumbling block to a more desirable ideal solution. The Hybrid Ant Lion Optimization (ALO) and Gray Wolf Optimization algorithm (GWO) combines elements from both ALO and GWO to benefit from their respective strengths and improve optimization performance. The HAM10000 dataset was utilized for this research, and it contains information on a variety of skin cancers, some of which are more common than others. The experimental analysis is done in python and the suggested model successfully distinguished between the two forms of skin cancer with an accuracy of 97.82%, recall of 96.80%, precision of 96.01%, and F1-score of 96.49%. The accuracy of ResNet is at 89.23, that of MLP at 90.87, of EfficientNet at 91.05, of ANN at 92.19, of IN3 at 93.73, and that of MobileNet at 94.90. The results shown that the suggested model outperforms baseline methods, providing substantial assistance to dermatologists and health specialists in the diagnosis of skin cancer.