Histopathology Image Analysis Utilizes Homology-Based Feature Extraction for Accurate Diagnosis of Various Lung Diseases

Histopathology image analysis is pivotal for diagnosing lung diseases, providing critical insights into tissue abnormalities for accurate medical assessments. Despite its significance, challenges arise from interpretational subjectivity and handling diverse tissue types, limiting its widespread application. The lung and colon histopathology dataset, comprising 25,000 color images, serves as a valuable resource in comprehensive medical research, offering diverse samples for detailed analysis and diagnosis. The proposed method introduces innovation by leveraging homology-based image processing for feature extraction, integrating the Unsharp Masking method in preprocessing. Classification through a Convolutional Neural Network (CNN) ensures a thorough and accurate analysis of histopathological images in lung disease diagnosis. Comparative analysis with existing approaches like 2D wavelet features and CRCNN-net underscores the undeniable superiority of the proposed method. The results demonstrate exceptional performance metrics, with an accuracy of 98.67%, precision at 93.70%, an F1-score of 80.22%, recall reaching 89.76%, and specificity standing at 90.82%. These outcomes affirm the proposed method's efficacy in advancing the field of histopathological lung disease diagnosis.