FSPBO-DQN: SeGAN based segmentation and Fractional Student Psychology Optimization enabled Deep Q Network for skin...

Skin cancer is one of the dangerous types of cancer and the rate of death is increasing due to the lack of knowledge in prevention and the symptoms. It is a common cancer type around the world and it occurs when the skin cells are damaged. Hence, the detection of skin cancer near the beginning is important to prevent the spread of cancer and to increase the survival rate. Recently, image processing and machine learning techniques gained more interest in medical applications. However, early analysis of skin cancer images is very challenging due to factors, like variations in the color illumination, light reflections from the skin surface, and different sizes and shapes of lesions. To detect skin cancer at an early stage and to increase the survival rate, an effective skin cancer detection method is introduced in this study using the proposed Fractional Student Psychology Based Optimization-based Deep Q Network (FSPBO-based DQN) in the wireless network scenario. At first, the nodes simulated in the network area are allowed to capture the healthcare information to make the detection strategy using the proposed method. Then, the routing is performed by the proposed Fractional Student Psychology Based Optimization (FSPBO) algorithm by considering the fitness parameters, like distance, energy, trust, and delay. After the images (healthcare information) are reached the Base Station (BS), the pre-processing, segmentation, and cancer detection processes are carried out to detect the skin lesions. Initially, the image is fed to pre-processing phase, where a Type II Fuzzy System and cuckoo search optimization algorithm (T2FCS) filter is employed to remove the noise of images. Then, the pre-processed images are fed to the segmentation phase, where speech enhancement Generative Adversarial Network (SeGAN) is used to generate the segmented results. Afterward, the Deep Q Network (DQN) detects the skin cancer based on the segmented results, and the training of DQN is made using the proposed FSPBO algorithm, which is designed by integrating the Student Psychology Based Optimization (SPBO) and Fractional Calculus (FC). The proposed method is more robust and reduces computation time and complexity. Moreover, the proposed method achieved higher performance by considering the measures, namely accuracy, sensitivity, and specificity with the values of 92.364%, 93.20%, and 92.63%.