BCCHI-HCNN: Breast Cancer Classification from Histopathological Images Using Hybrid Deep CNN Models

• Published in: Journal of digital imaging Vol. 38; no. 3; pp. 1690 - 1703
• Main Authors: Pandey, Saroj Kumar, Rathore, Yogesh Kumar, Ojha, Manoj Kumar, Janghel, Rekh Ram, Sinha, Anurag, Kumar, Ankit
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.06.2025; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial neural networks; Breast cancer; Breast Neoplasms - classification; Breast Neoplasms - diagnostic imaging; Breast Neoplasms - pathology; Decision Trees; Deep Learning; Expression vectors; Female; Health care; Humans; Image Interpretation, Computer-Assisted - methods; Imaging; Machine learning; Medicine; Medicine & Public Health; Neural networks; Neural Networks, Computer; Original Paper; Principal Component Analysis; Principal components analysis; Public health; Radiology; Support Vector Machine; Support vector machines; Transfer learning; Principle component analysis (PCA); Breast cancer; Transfer learning; Histopathological images
• ISSN: 2948-2933; 0897-1889; 2948-2925; 2948-2933; 1618-727X
• DOI: 10.1007/s10278-024-01297-2

Breast cancer is the most common cancer in women globally, imposing a significant burden on global public health due to high death rates. Data from the World Health Organization show an alarming annual incidence of nearly 2.3 million new cases, drawing the attention of patients, healthcare professionals, and governments alike. Through the examination of histopathological pictures, this study aims to revolutionize the early and precise identification of breast cancer by utilizing the capabilities of a deep convolutional neural network (CNN)-based model. The model’s performance is improved by including numerous classifiers, including support vector machine (SVM), decision tree, and K -nearest neighbors (KNN), using transfer learning techniques. The studies include evaluating two separate feature vectors, one with and one without principal component analysis (PCA). Extensive comparisons are made to measure the model’s performance against current deep learning models, including critical metrics such as false positive rate, true positive rate, accuracy, precision, and recall. The data show that the SVM algorithm with PCA features achieves excellent speed and accuracy, with an amazing accuracy of 99.5%. Furthermore, although being somewhat slower than SVM, the decision tree model has the greatest accuracy of 99.4% without PCA. This study suggests a viable strategy for improving early breast cancer diagnosis, opening the path for more effective healthcare treatments and better patient outcomes.