Automatic skin tumor detection in dermoscopic samples using Online Patch Fuzzy Region Based Segmentation

Skin tumor detection and classification have an important role which is applied in the field of research, particularly in the field of medical diagnosis. The classification of tumors in skin cells is of more significance since the number of affected people is increasing. The focus of this research work is to come up with a new and efficient method of enhancing skin images as well as identifying tumors from other areas on computed tomographic skin images. This work is mainly concerned with medical application methods on computed tomography (CT) skin tumor images that are developed and applied effectively. The first step is acquiring images. It can be seen that the Boosted Notch Diffusion Filtering − Mean Pixel Histogram Equalization (BNDF-MPHE) algorithm serves as the preprocessing step within the context of the presented model. The proposed step involves Superpixel Contour Metric Segment Clustering (SCMSC) followed by an Online Patch Fuzzy Region Based Segmentation (OPFRBS) Algorithm for effective segmentation of the skin tumor cells with an accuracy of 99.25% for benign and 97.39% for malignant tumors respectively. The time required for processing the lesion is less than 2 sec. The proposed method uses MATLAB 2024a workbench and accuracy is quite higher compared with other existing algorithms for both benign and malignant samples respectively. The proposed research methodology has been validated with real-time clinical samples effectively and throws light on the patient’s life to resume normalcy and live long.