Automated Classification of Histopathology Images Using Transfer Learning
This work addresses the need for faster and more accurate disease diagnosis in histopathology, though it is incremental as it applies existing transfer learning methods to this domain.
The study tackled automated classification of histopathology images to improve diagnostic quality and reduce analysis time, achieving classification accuracies of 97.89% with DenseNet-161 on grayscale images and 98.87% with ResNet-50 on color images, outperforming state-of-the-art methods across 24 categories.
There is a strong need for automated systems to improve diagnostic quality and reduce the analysis time in histopathology image processing. Automated detection and classification of pathological tissue characteristics with computer-aided diagnostic systems are a critical step in the early diagnosis and treatment of diseases. Once a pathology image is scanned by a microscope and loaded onto a computer, it can be used for automated detection and classification of diseases. In this study, the DenseNet-161 and ResNet-50 pre-trained CNN models have been used to classify digital histopathology patches into the corresponding whole slide images via transfer learning technique. The proposed pre-trained models were tested on grayscale and color histopathology images. The DenseNet-161 pre-trained model achieved a classification accuracy of 97.89% using grayscale images and the ResNet-50 model obtained the accuracy of 98.87% for color images. The proposed pre-trained models outperform state-of-the-art methods in all performance metrics to classify digital pathology patches into 24 categories.