TransAdaptNet: a transformer-based adaptive learning model for accurate Monkeypox detection and classification

Abstract

The rise of Monkeypox (MPX) as a global health issue requires efficient and swift diagnostic techniques. In this paper, a Transformer-Based Adaptive Learning (TransAdaptNet) framework is proposed to enhance MPX detection and classification. The framework includes advanced AI methodologies, such as transformer-based models and adaptive learning systems, to significantly enhance the accuracy and efficiency of diagnosis. The proposed TransAdaptNet consists of two modules which are; data preprocessing, and patient classification. Through data preprocessing module, the used data set are preprocessed through several stages; remove/fill null values, remove outlier items, feature extraction and selection. A new feature selection methodology called Improved Whale Optimization Algorithm (IWOA)is introduced within the preprocessing pipeline. IWOA consists of two phases which are; Multi-Selection Phase (MSP) using two filter methods which are; fisher score and chi-square while Final Selection Phase (FSP) using Binary Whale Optimization Algorithm (BWOA) with union operations. Then, these features are fed into proposed patient classification module. TransAdaptNet achieved an exceptional accuracy of 98.7% utilizing a publicly accessible dataset comprising 500 monkeypox-positive and negative cases, surpassing conventional models like Random Forest and XGBoost. The framework has proven its ability to perform calculations quickly, avoiding the high costs associated with complex architectures such as transformers and attention mechanisms. TransAdaptNet produces clear outputs, enhancing the clarity of its predictions. The modular design ensures applicability in diverse healthcare settings, facilitating implementation. This method overcomes the limitations of traditional diagnostic tools, providing an effective and reliable means of identifying and mitigating MPX outbreaks at an early stage.