An Optimized Method for Skin Cancer Diagnosis Using Modified Thermal Exchange Optimization Algorithm

Skin cancer is the most common cancer of the body. It is estimated that more than one million people worldwide develop skin cancer each year. Early detection of this cancer has a high effect on the disease treatment. In this paper, a new optimal and automatic pipeline approach has been proposed for the diagnosis of this disease from dermoscopy images. The proposed method includes a noise reduction process before processing for eliminating the noises. Then, the Otsu method as one of the widely used thresholding method is used to characterize the region of interest. Afterward, 20 different features are extracted from the image. To reduce the method complexity, a new modified version of the Thermal Exchange Optimization Algorithm is performed to the features. This improves the method precision and consistency. To validate the proposed method's efficiency, it is implemented to the American Cancer Society database, its results are compared with some state-of-the-art methods, and the final results showed the superiority of the proposed method against the others.

Automated summary

Skin cancer is the most common form of cancer, with over one million cases diagnosed worldwide each year. Early detection is crucial for effective treatment. This study introduces a new automated pipeline approach for diagnosing skin cancer from dermoscopy images, using noise reduction, thresholding, feature extraction, and a modified thermal exchange optimization algorithm to improve precision and consistency. Validation on the American Cancer Society database demonstrates the superior performance of this method compared to existing state-of-the-art methods.