Skin cancer detection and segmentation utilising LadderNet by stock exchange white shark optimisation enabled deep learning

Skin cancer is one of the most common types of cancer worldwide, with increasing incidence rates over the past few decades. This paper proposes an optimisation-enabled deep learning for skin cancer segmentation and detection. Initially, the input image is pre-processed by a bilateral filter. After that, skin lesion segmentation is performed using LadderNet, which is tuned by Stock exchange trading white shark optimisation (SEWSO). Here, SEWSO is the combination of Stock exchange trading optimisation (SETO) and white shark optimisation (WSO). Moreover, segmented image is allowed through data augmentation which is done by rotation, shifting and random brightness techniques. Thereafter, the feature extraction is achieved to obtain the desired features. At last, the feature vector is subjected to skin cancer detection, which is accomplished by employing SqueezeNet tuned by SEWSO. This approach delivered high accuracy, sensitivity, and specificity of 93.90%, 95.00%, and 94.70%, respectively.