Monkeypox Disease Classification Using HOG-SVM Model

Monkeypox is a viral disease that has caused outbreaks in different parts of the world through human-to-human transmission. The increasing number of cases of monkeypox in recent years highlights the need for timely detection to prevent further spread and ensure prompt treatment. In our research paper, we have employed the latest technology of AI through machine learning models for the detection of monkeypox. Our aim is to reduce computational time and enhance accuracy by identifying images as either monkeypox or non-monkeypox. We reviewed various research studies in this field and found that most of them are based on deep learning, which makes them computationally complex. After training and testing our model using available datasets, we evaluated its performance using parameters such as accuracy, precision, F1 score, and recall for each extraction technique. Our experiments yielded an accuracy of 0.94, an F1 score of 0.95, a precision of 0.93, and a recall of 0.97. Our newly developed model will be highly beneficial in the medical field, particularly for countries with large populations such as China, India, and the United States, for the detection of monkeypox on a mass scale.