A novel optimized machine learning approach with texture rectified cross-attention based transformer for COVID-19 detection

Abstract

Distinguishing COVID-19 from other acute infectious pneumonia types remains a significant challenge, often complicated by overlapping symptoms and radiological features. This paper introduces a novel texture-based detection framework that enhances the classification accuracy of COVID-19 cases. The proposed method begins with a pre-processing task carried out by the Advanced Bilinear CLAHE (ABC) approach. This will enhance the image contrast and reduce noise revealing critical texture features than traditional methods. Here, one of the innovations is said to be the Texture Rectified Cross-Attention Transformer (TRCAT) for feature extraction. Unlike previous models, TRCAT integrates both handcrafted and deep features by texture enhancement block along with an attention-rectified convolutional block. This combination allows more detailed feature extraction enabling the model to differentiate variations in texture across different cases of pneumonia and COVID-19. Further, Mountain Gazelle Optimized Enhanced Support Vector Machine (MGO-ESVM) optimized by the Enhanced Chaotic Mountain Gazelle Optimization (EMGO) algorithm is introduced for classification. This optimization minimizes processing losses and enhances the accuracy of the classification, particularly in distinguishing COVID-19 from other forms of pneumonia. The method was evaluated on the COVID-19 Detection X-Ray Dataset, and the results are outstanding, with an accuracy rate of 99.34 %. Compared to other methods, namely ensemble CNN, this framework offers significantly improved performance in COVID-19 detection. For instance, in similar studies, accuracy rates have typically ranged between 95 % and 98.82 %. From the analysis, the TRCAT and MGO-ESVM framework consistently outperforms these methods by a margin of 1–2 %. These results underline the novelty and effectiveness of the proposed texture-based approach in enhancing diagnostic precision and generalizability.