SKIN CANCER CLASSIFICATION USING DEEP LEARNING

Abstract

Timely detection is paramount in the effective management of skin cancer, emphasizing the pivotal role of precise diagnostic tools. A resilient medical decision support system, proficient in categorizing skin lesions based on dermoscopic images, serves as a fundamental tool in assessing the prognosis of this condition. Despite the intricate manifestations across different forms of skin cancer, recent strides in Deep Convolutional Neural Networks (DCNN) have markedly bolstered the capability to discern diverse cancer types from dermoscopic imagery. These advancements in DCNNs have revolutionized the field of dermatology,[6]. enabling more accurate and efficient classification of skin lesions. By leveraging the power of deep learning, researchers have been able to develop models that can not only distinguish between benign and malignant lesions but also classify specific types of skin cancer with high accuracy. This level of precision is crucial in ensuring that patients receive timely and appropriate treatment, ultimately improving outcomes and reducing mortality rates associated with skin cancer[4]. Furthermore, the development of robust medical decision support systems based on DCNNs has the potential to alleviate the burden on healthcare professionals by providing them with reliable tools for assisting in diagnosis and treatment planning. As these technologies continue to evolve, they are likely to play an increasingly important role in the early detection and management of skin cancer, ultimately saving lives and improving patient care[4]. Numerous machine learning methodologies have emerged, aiming for refined skin cancer diagnosis leveraging medical images, with a notable reliance on pre-trained Convolutional Neural Networks (CNNs) to surmount the hurdle of limited training data. However, the scarcity of malignant tumor samples often constrains these models, impeding classification accuracy. This study's principal objective is to craft a model proficient in accurately distinguishing between melanoma and non-melanoma skin cancer variants[12]. To this end, we propose an optimized architecture rooted in NASNet, augmented by the integration of supplementary data and the inclusion of an additional foundational layer within the CNN framework. This proposed approach fortifies the model's adaptability to incomplete and disparate data instances, thereby advancing its efficacy in skin cancer classification[4]. The integration of supplementary data, such as clinical information and patient history[15][25], 5 serves to enrich the model's understanding of the context surrounding each image, enhancing its ability to make accurate classifications. Additionally, the inclusion of an additional foundational layer within the CNN framework allows the model to capture more intricate patterns and features within the data, further improving its classification performance. By combining these elements with the powerful architecture of NASNet, we aim to develop a model that not only achieves high accuracy in distinguishing between melanoma and non melanoma skin cancer but also demonstrates robustness in handling variations and complexities within the dataset[4]. Moreover, the proposed approach holds promise for addressing the challenge of limited training data by enhancing the model's ability to generalize from the available samples. By improving the model's adaptability to incomplete and disparate data instances, we aim to create a more resilient and effective tool for skin cancer classification. This study contributes to the ongoing efforts in leveraging machine learning and deep learning techniques for enhancing medical diagnostics, particularly in the field of dermatology[6].