Recent Papers

In this paper, Improving biopsy image classification often involves employing advanced feature extraction techniques to extract relevant information from the images. These techniques help in capturing important patterns and structures that can aid in accurate classification. Here are some feature extraction techniques commonly used in medical image analysis, including biopsy image classification. Breast cancer (BC) classification has become a point of concern within the field of biomedical informatics in the health care sector in recent years. This is because it is the second-largest cause of cancer-related fatalities among women. The medical field has attracted the attention of researchers in applying machine learning techniques to the detection, and monitoring of life-threatening diseases such as breast cancer (BC). Automatic detection of BC based on pathological images and the use of a Computer-Aided Diagnosis (CAD) system allow doctors to make a more reliable decision. we propose a colon biopsy image classification system called CBIC, which benefits from discriminatory capabilities of information rich hybrid feature spaces, and performance enhancement based on ensemble classification methodology.In this work, handcrafted feature extraction techniques (Hu moment, Haralick textures, and color histogram) and Deep Neural Network (DNN) are employed for breast cancer multi-classification using histopathological images on the BreakHis dataset. The features extracted using the handcrafted techniques are used to train the DNN classifiers with four dense layers and Softmax. Further, the data augmentation method was employed to address the issue of overfitting. The results obtained reveal that the use of handcrafted approach as feature extractors and DNN classifiers had a better performance in breast cancer multi classification than other approaches in the literature.

Copyright © 2024 Bindu S. This is an open access article distributed under the Creative Commons Attribution License.