Published Paper Details:

Monkeypox is an infectious disease caused by the monkeypox virus (MPXV) that primarily affects animals but can be transmitted to humans, resulting in serious health implications. Early detection and accurate diagnosis of the disease are crucial for effective containment and treatment. Controlling the rapid transmission of the disease necessitates timely and accurate diagnosis, but the availability of traditional confirmatory tests, such as Polymerase Chain Reaction (PCR), remains limited. In this challenging scenario, by leveraging advanced technology, this approach can present a valuable alternative to conventional testing methods. To address this issue, we present a novel web application developed using Python's Flask framework, employing deep learning techniques for monkeypox disease detection. In this project, we develop a novel approach for the detection of Monkeypox disease using Deep Learning, specifically leveraging the ResNet50V2 architecture. The core of our approach relies on the ResNet50V2 architecture, which has demonstrated impressive performance in medical image analysis tasks. The model was trained on the Monkeypox Skin Lesion Dataset (MSLD), consisting of 1428 images labelled as 'Monkeypox' and 1764 images labelled as 'Others,' encompassing various skin lesion types. Through rigorous training and validation, our deep learning model achieved remarkable results, with a training accuracy of 93.00% and a validation accuracy of 92.00%. The easy-to-use web application allows users to upload skin lesion images, which are then analysed by our trained model to provide rapid identification of monkeypox. By providing a consumer-level software solution, our project aims to empower individuals and healthcare practitioners with a reliable tool for early detection of monkeypox, facilitating prompt action and containment efforts. This system can play a pivotal role in mitigating the impact of the ongoing monkeypox outbreak and enhancing global public health preparedness in the face of infectious diseases.