Explainable clinical diagnosis through unexploited yet optimized fine-tuned ConvNeXt Models for accurate monkeypox disease classification.

Deep learning (DL) has had an incredible influence on many different scientific areas over the past couple of decades. Particularly in the field of healthcare, DL strategies were able to outclass other existing methodologies in image processing. The rapid expansion of the monkeypox endemic to over 40 nations apart from Africa has prompted serious worries in the realm of public health. Given that monkeypox can have symptoms that are akin to both chickenpox and measles, early detection can be difficult. Fortunately, due to the developments in artificial intelligence approaches, it can be implemented to promptly and accurately identify monkeypox disease using visual data information. Many DL driven techniques have already been exploited in the literature for skin related issues, which have provided accurate results to some extent. These models were dependent on extensive computational and time resources due to which the real-time applicability is difficult. Rather of building and training CNNs from scratch, this study uses transfer learning (TL) technique to fine-tune pre-trained networks, particularly exploiting various versions of ConvNeXt, by substituting last layer with additional task specific ones. A number of pre-processing and data augmentation methods have also been assessed and adjusted with regard to computing time and performance. The proposed study performs the binary and multi class monkeypox disease classification task. Promising accurate results of 99.9 % on the benchmark MSLD (binary class) dataset and 94 % on the MSLD v2.0 (multi-class) dataset is obtained by fine-tuned TL-based ConvNeXtSmall and ConvNeXtBase architecture with Adafactor optimization technique, demonstrating the practicality of the suggested framework as a substitute for the current ones. The proposed model is assessed through both standard train-test split and k-fold cross validation techniques. Furthermore, performance of models is also assessed on several other metrics including recall, F1 score, precision and multiple statistical tests incorporated with explainable AI methods for better interpretability of results. The concerns regarding the real-time applicability are tackled by utilizing the less time consuming and computationally efficient networks through the exploitation of transfer learning capabilities. Moreover, the explainable findings of the proposed study will be highly valuable for the healthcare professionals to understand the decisive behavior of the model and make informed clinical decisions.