IMATX: An Integrated Multi-Context Pyramidal Framework for Explainable and Interpretable AI Predictions for Real time Clinical Validation in Cervical Cancer Detection

The detection of cervical cancer through histopathological images remains difficult due to complex cellular features combined with diverse staining practices which make current methods ineffective in clinical settings. Automated classification systems need to identify significant spatial features along with contextual features because this leads to more accurate and dependable diagnoses. Present methods show limitations when identifying complex associations between elements thereby reducing their escalating characteristics and broad applicability. This paper introduces the deep learning architecture IMATX Net which combines IMA attention modules with T-blocks for improved feature selection and classification accomplishments. Effective lesion discrimination alongside interpretability emerges from the proposed network because it refines both spatial and contextual elements. The system operates through a multiple-stage procedure which integrates attention channelling together with feature refinement along with classification steps. Through the IMA layer monitoring of attention the model creates better explainability by marking down crucial diagnostic regions. The ablation study evaluates all vital network components to show their effects on classification results. IMATX Net produces higher performance than current machine learning (ML) and deep learning (DL) systems while delivering maximum sensitivity and specificity and accuracy and precision and F1-score. The reliability of the model gets measured through confusion matrix (CM) along with ROC-AUC curves yet training and validation curves prove the learning stays stable even with minimal overfitting. IMATX Net demonstrates a sensitivity value of 0.97 which exceeds all other state-of-the-art techniques. The experimental findings show that IMATX Net demonstrates effective performance in addressing cervical cancer detection problems in histopathological imaging. The proposed model delivers robust interpretable clinical-scale classification through its integration of multi-scale attention features with refinement methods. The research verifies feature refinement techniques utilizing attention mechanisms as crucial elements for medical image analysis while allowing future improvements in automatic cervical cancer screening methods.