International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 05 | May 2024
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p-ISSN: 2395-0072
Skin Cancer Type Classification and Nutritional Diet Recommendation using AI & ML Indrajith K1, Livia Christopher2, Kuriakose Baby3, V Ranjanapriya4 1,2,3,4 Department of CSE, SNGCE
------------------------------------------------------------------------***------------------------------------------------------------------------Abstract— Skin cancer is a prevalent and potentially lifethreatening condition that necessitates early detection and personalized interventions for effective treatment. This project presents a comprehensive approach to address this issue by combining machine learning (ML) and artificial intelligence (AI) techniques for skin cancer type classification and nutritional diet recommendation This project combines machine learning (ML) and artificial intelligence (AI) to address skin cancer diagnosis and management. The ML model accurately classifies skin cancer types using deep learning on diverse lesion images. Additionally, an AI-based nutritional recommender system tailors diet plans based on cancer type, health history, and preferences. The integrated solution aims to improve patient outcomes by enabling early detection, precise classification, and personalized nutritional support, presenting a valuable tool for healthcare professionals and patients alike. Clinical trials and user feedback validate the system's effectiveness in real-world healthcare scenarios.
this gap by scrutinizing a diverse array of skin lesion images. This approach ensures a reliable and precise classification system that aids healthcare professionals in tailoring treatment plans to the specific skin cancer type, thereby enhancing the overall efficacy of interventions. Beyond the realm of diagnostics, our project acknowledges the pivotal role of nutrition in supporting skin health and mitigating cancer risks. An AI-based nutritional recommendation system is designed to craft personalized diet plans, taking into account the individual's skin cancer type, health history, and dietary preferences. This holistic approach aims to not only treat the disease but also promote overall well-being, acknowledging the interconnectedness of nutrition and health. The amalgamation of ML, AI, and healthcare in this project signifies a paradigm shift towards personalized and targeted interventions in the field of dermatology. As we embark on this journey, the effectiveness of our integrated solution will be rigorously validated through clinical trials and user feedback, ensuring its practical applicability in real-world healthcare settings. By contributing to the advancement of precision medicine for skin cancer, our project aspires to usher in a new era of patient-centric care and improved outcomes.
Index Terms — Skin cancer, Artificial intelligence, Machine Learning, Healthcare I. INTRODUCTION Skin cancer remains a significant public health concern globally, necessitating innovative approaches for early detection and personalized care. This project introduces a novel paradigm in skin cancer diagnosis and management by harnessing the power of machine learning (ML) and artificial intelligence (AI). The integration of sophisticated image analysis techniques for precise skin cancer type classification and an AIdriven nutritional recommendation system forms the cornerstone of our endeavor, offering a comprehensive solution to address the complexities of skin cancer. The diversity in skin cancer types, including melanoma, squamous cell carcinoma, and basal cell carcinoma, underscores the need for accurate and efficient diagnostic tools. Conventional methods often fall short in providing personalized insights into cancer types, leading to generalized treatment approaches. Our ML model, leveraging deep learning algorithms, strives to fill
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