Enhancing Monkey Pox Detection with Deep Interpretable Techniques

A serious worldwide health concern is monkey-pox, a viral disease that resembles smallpox in its symptoms. For containment and treatment to be effective, a quick and accurate diagnosis is essential. Conventional diagnostic techniques, such PCR and ELISA, are frequently inaccessible in environments with limited resources because they call for specialized labs. This research introduces a groundbreaking method employing deep learning techniques for the early detection of the monkey-pox virus. Our research leverages a substantial data set comprising of 2000 images of mpox infected and normal images from diverse sources. This enables us to comprehensive training of AlexNet and DenseNet algorithms, a image classification techniques to analyze the features present in pixels. The model's inference process will be displayed through the use of these deep learning techniques, which will offer insights into the salient characteristics that go into the diagnosis. The constructed model will be assessed based on its interpretability, sensitivity, specificity, and accuracy. The practical usefulness of the model will be evaluated by taking into account real-world scenarios, such as point-of-care diagnostics or distant healthcare settings. The research's conclusions could completely change the way monkey-pox is diagnosed, allowing for early identification, better disease control, and eventually better public health results.