Monkeypox Diagnosis with Interpretable Deep Learning

IEEE BASE PAPER TITLE:

Monkeypox Diagnosis with Interpretable Deep Learning

(OR)

OUR PROPOSED PROJECT TITLE:

Monkeypox Disease Detection using Deep Learning

IEEE BASE PAPER ABSTRACT:

As the world gradually recovers from the impacts of COVID-19, the recent global spread of Monkeypox disease has raised concerns about another potential pandemic, highlighting the urgency of early detection and intervention to curb its transmission. Deep Learning (DL) based disease prediction presents a promising solution, offering affordable and accessible diagnostic services. In this study, we harnessed Transfer Learning (TL) techniques to tweak and assess the performance of an array of six different DL models, encompassing VGG16, InceptionResNetV2, ResNet50, ResNet101, MobileNetV2, VGG19, and Vision Transformer (ViT). Among this diverse collection, it was the modified versions of the VGG19 and MobileNetV2 models that outshone the others, boasting striking accuracy rates ranging from an impressive 93% to an astounding 99%. Our results echo the findings of recent research endeavours that similarly showcased enhanced performance when developing disease diagnostic models armed with the power of TL. To add to this, we made use of Local Interpretable Model Agnostic Explanations (LIME) to lend a sense of transparency to our model’s predictions, and to identify the crucial features correlating with the onset of Monkeypox disease. These findings offer significant implications for disease prevention and control efforts, particularly in remote and resource-limited areas.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED:

ResNet50V2 Architecture.

OUR PROPOSED ABSTRACT:

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 labeled as ‘Monkeypox’ and 1764 images labeled 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 analyzed 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.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

• System : Pentium i3 Processor.
• Hard Disk : 500 GB.
• Monitor : 15’’ LED
• Input Devices : Keyboard, Mouse
• Ram : 6 GB

SOFTWARE REQUIREMENTS: 

• Operating system : Windows 10 / 11.
• Coding Language : Python 3.10.9.
• Web Framework : Flask.
• Frontend : HTML, CSS, JavaScript.

REFERENCE:

MD MANJURUL AHSAN, MD SHAHIN ALI, MD. MEHEDI HASSAN, TAREQUE ABU ABDULLAH, KISHOR DATTA GUPTA, ULAS BAGCI, CHETNA KAUSHAL, NAGLAA F. SOLIMAN, “Monkeypox Diagnosis with Interpretable Deep Learning”, IEEE Access ( Early Access ), 2023.

👉CLICK HERE TO BUY THIS PROJECT SOURCE CODE👈