<p>Efficient and precise diagnosis and classification of skin diseases remain crucial for achieving favorable results in the field of medical diagnostics. The study investigates deep learning methods for the detection and classification of various skin illnesses, such as measles, monkeypox, and skin cancer. The primary objective of the study is to identify monkeypox in its initial phases. Deep learning algorithms possess the imminent ability to identify skin infections, such as monkeypox, in order to hinder their further transmission. The primary goal of this project is to create a deep learning model utilizing image processing techniques to accurately classify and identify various skin conditions. During the early stage of the preprocessing procedure, various color version techniques and data augmentations were utilized to mitigate the danger of overfitting the proposed model. The current study employs a modified segmentation techniquehat utilizes multi-thresholding to divide the image into different levels. This technique uses the mean and variance of the image to determine the appropriate threshold. The recursive approach is utilized on subranges obtained from previous steps to ascertain the thresholds and identify new ideal subranges for subsequent phases. Moreover, transfer learning was utilized to apply pre-processed images by employing seven separate pre-trained deep learning models. The model with the performance matrices was selected and suggested by refining the model that produced the highest performance. Furthermore, the proposed MpoxSegNet model evaluated a modified VGGNet-16 DL-based CNN model, giving remarkable accuracy rates commencing at 98.64 for binary and 99.47%. for multiclassificationThe results of this study will prominently support clinicians in classifying and diagnosing monkeypox disease.</p>