MalenoCare: Skin Cancer Detection and Prescriptive Guild lines using AI and ML

MalenoCare is an AI-powered healthcare solution designed to assist in the early detection and stage prediction of skin cancer using deep learning techniques. The system leverages convolutional neural networks (CNNs) to analyze dermoscopic images and accurately classify types of skin cancer. It offers a user-friendly interface for both patients and doctors, enabling remote diagnosis, medical report generation, and real-time chat communication. By integrating machine learning with accessible digital tools, MalenoCare aims to support timely intervention, reduce diagnostic delays, and contribute to improved patient outcomes, especially in underserved or remote areas. The project utilizes publicly available datasets such as HAM10000 and ISIC 2019 to train and validate the model, ensuring high accuracy and generalization. Advanced techniques like data augmentation, normalization, and transfer learning are applied to enhance performance. MalenoCare also features a medical history form, secure storage of patient data, and intelligent report generation. Its vision extends beyond diagnosis—aiming to spread awareness, promote preventive care, and align with global health goals by making skin cancer screening accessible and affordable to all

Authors

Publication Details

Journal
International Journal for Research in Applied Science and Engineering Technology
Published
2025-06-09
DOI
https://doi.org/10.22214/ijraset.2025.72226
Primary Topic
Cutaneous Melanoma Detection and Management
Type
article
Field-Weighted Citation Impact
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article

MalenoCare: Skin Cancer Detection and Prescriptive Guild lines using AI and ML

More Nikhil Sharad
International Journal for Research in Applied Science and Engineering Technology
Cutaneous Melanoma Detection and Management
article

MalenoCare: Skin Cancer Detection and Prescriptive Guild lines using AI and ML

More Nikhil Sharad
article en

Abstract

MalenoCare is an AI-powered healthcare solution designed to assist in the early detection and stage prediction of skin cancer using deep learning techniques. The system leverages convolutional neural networks (CNNs) to analyze dermoscopic images and accurately classify types of skin cancer. It offers a user-friendly interface for both patients and doctors, enabling remote diagnosis, medical report generation, and real-time chat communication. By integrating machine learning with accessible digital tools, MalenoCare aims to support timely intervention, reduce diagnostic delays, and contribute to improved patient outcomes, especially in underserved or remote areas. The project utilizes publicly available datasets such as HAM10000 and ISIC 2019 to train and validate the model, ensuring high accuracy and generalization. Advanced techniques like data augmentation, normalization, and transfer learning are applied to enhance performance. MalenoCare also features a medical history form, secure storage of patient data, and intelligent report generation. Its vision extends beyond diagnosis—aiming to spread awareness, promote preventive care, and align with global health goals by making skin cancer screening accessible and affordable to all

International Journal for Research in Applied Science and Engineering TechnologyVol. 13(6)
Partnerships for the goals
Openalex Percentile: Top 80%
Cutaneous Melanoma Detection and Management
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