According to the American cancer society, the average risk of women getting diagnosed with breast cancer during their life is 13%. The World Health Organization also reports that the number of cancer cases is projected to rise to 19.3 million by 2025. Recent research works point out that physicians can only diagnose cancer with 79% accuracy while machine learning procedures achieve 91% accuracy or more. The current challenges are early cancer detection and the efficient and accurate diagnosis of histopathology tissue samples. Several Deep Learning breast cancer classification models have been developed to assist medical practitioners. However, these methods are data hungry and require thousands of training image samples, often coupled with data augmentation to achieve satisfactory results with long training hours. In this paper, we propose a machine learning classification model by integrating the Parameter free Thresholding Adjacency Statistics (PFTAS) with Fibonacci-p patterns for breast cancer detection. Computer simulations on BreakHis cancer datasets in comparison to other machine learning and deep learning-based methods show that (i) the presented method helps eliminate dependence on large training data and data augmentation, (ii) robustness to noise and background stains, and (iii) lightweight model easy to train and deploy.
The goal of this paper is to (a) test the nuclei based Computer Aided Cancer Detection system using Human Visual based system on the histopathology images and (b) Compare the results of the proposed system with the Local Binary Pattern and modified Fibonacci -p pattern systems. The system performance is evaluated using different parameters such as accuracy, specificity, sensitivity, positive predictive value, and negative predictive value on 251 prostate histopathology images. The accuracy of 96.69% was observed for cancer detection using the proposed human visual based system compared to 87.42% and 94.70% observed for Local Binary patterns and the modified Fibonacci p patterns.
The goals of this paper are (1) test the Computer Aided Classification of the prostate cancer histopathology images based on the Bag-of-Words (BoW) approach (2) evaluate the performance of the classification grade 3 and 4 of the proposed method using the results of the approach proposed by the authors Khurd et al. in  and (3) classify the different grades of cancer namely, grade 0, 3, 4, and 5 using the proposed approach. The system performance is assessed using 132 prostate cancer histopathology of different grades. The system performance of the SURF features are also analyzed by comparing the results with SIFT features using different cluster sizes. The results show 90.15% accuracy in detection of prostate cancer images using SURF features with 75 clusters for k-mean clustering. The results showed higher sensitivity for SURF based BoW classification compared to SIFT based BoW.