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Top1. Introduction
Automated medical image segmentation has been extensively studied in the medical image analysis field since radiologists usually have to manually look for malignancy in a pool of images and match cancer-related features to candidate tumors (Hesamian et al., 2019b). In this process of diagnosis, some features may get easily missed in many cases and there also exist some important features that cannot be extracted visually but can be with an automated system. The disagreement among different radiologists in the segmentation of images is reported to be between 2% to 49% (Hesamian et al., 2019b). All these facts are the primary motivation for designing a computer aided segmentation model for segmenting medical images accurately. As per 2022 statistics related to cancer article (Siegel et al., 2022), approximately 1,918.030 cancer cases will be detected of which 99,780 will be of melanoma skin cancer. A report by (Siegel et al., 2021), the death rate of cancer has dropped from its peak in 1991 to 2018 because of a reduction in smoking and the development of models that lead to early detection and medication of cancer. If detected in the early stages the survival rate for melanoma skin cancer is 93% according to 2021 statistics (Siegel et al., 2021). The unconstrained development of abnormal cells leads to skin cancer which can further spread to the rest of the organs of the body. Cancer of the skin is often defined as fatal melanoma or benignant (Wei et al., 2019). Among various kinds of skin cancers, melanoma cancer is of utmost deadly, accounting for a large percentage of skin cancer deaths(Siegel & Miller, 2019). Because of the fatal nature of melanoma, it has attracted ample research and clinical attention.
Photography, dermoscopy, confocal scanning laser microscopy (CSLM), optical coherence tomography (OCT), ultrasonography, magnetic resonance imaging (MRI), and spectroscopic imaging are presently utilized to help dermatologists in skin lesion identification (Hasan et al., 2020) . Dermatologists commonly visually scrutinize the produced photos using the specified procedures to detect cancerous skin, which is typically thought to be a laborious and time consuming task. Dermoscopy, which has been in use for over 20 years, has increased the diagnostic rate when compared to viewable surveillance solely (Mayer, 1997). The ABCD benchmark assists non-professionals in distinguishing benignant melanocytic naevi from melanoma while screening skin lesions (Abbasi et al., 2004). End-to-end computerized technologies that can precisely segment skin lesions of all sorts are very appropriate to emulate the clinical ABCD benchmark. Computer aided diagnostic programs have been designed to support medical experts and enhance accuracy. In many diagnostic centers, computer aided diagnosis has become the habitual clinical practice for diagnosing abnormal growth of lesions in medical images. Computer aided technologies for dermoscopic medical images are typically most of the time made of more than one unit including image acquisition, image pretreatment, segmenting images, feature mining, and classification units (Fan et al., 2017)(Jalalian et al., 2017). The precise segmentation of lesions in Dermoscopy images from the normal skin serves a significant role in gaining unique and exemplary characteristics of melanoma areas of interest in Dermoscopy images(Wei et al., 2019). Several skin lesion segmentation models (Yuan, 2017)(Ebenezer & Rajapakse, 2018)(Berseth, 2017)(Bi et al., 2017) that utilize deeper neural networks with convolutional layers have been developed and have substantially improved the outcome of segmentation. Though, automatically segmenting skin lesions from their background is still considered an open research problem or a challenging problem because of the following reasons: