Vandana - ^1,2 Chetna Sharma ^1* Yonis Gulzar ^3* Mohammad Shuaib Mir ³

• ¹ Chitkara University, Chandigarh, Punjab, India
• ² Mukand Lal National College, Haryana, India
• ³ King Faisal University, Al-Ahsa, Saudi Arabia

The monkeypox epidemic has spread to nearly every nation. For effective handling and treatment, early identification and diagnosis of monkeypox using digital skin lesion images is critical. This article presents a supervised learning-based classification method designed for the precise identification of monkeypox cases using deep learning. The analysis was conducted using an opensource dataset from Kaggle, consisting of digital images of monkeypox, which were processed using advanced image processing and deep learning techniques. The data was categorized based on findings related and unrelated to monkeypox. Resnet with 50 layers and up to 35 folds was utilized to identify regions of interest, which could be indicative of characteristics relevant to computer-assisted medical diagnosis and enable us to solve image processing and natural language processing tasks with high accuracy. In terms of performance, the accuracy of 96% and precision is 95% achieved during cross-validation classification testing. This outcome demonstrates the potential for computer-assisted diagnosis as a supplementary tool for medical professionals. Amid the monkeypox outbreak, this method offers a technical and objective assessment of patients' skin conditions, thereby simplifying the diagnostic process for specialists.