A Clinical Decision Support Tool to Detect Invasive Ductal Carcinoma in Histopathological Images Using Support Vector Machines, Naïve-Bayes, and K-Nearest Neighbor Classifiers

Abstract

This study aims to describe a model that will apply image processing and traditional machine learning techniques specifically Support Vector Machines, Naïve-Bayes, and k-Nearest Neighbors to identify whether or not a given breast histopathological image has Invasive Ductal Carcinoma (IDC). The dataset consisted of 54,811 breast cancer image patches of size 50px x 50px, consisting of 39,148 IDC negative and 15,663 IDC positive. Feature extraction was accomplished using Oriented FAST and Rotated BRIEF (ORB) descriptors. Feature scaling was performed using Min-Max Normalization while K-Means Clustering on the ORB descriptors was used to generate the visual codebook. Automatic hyperparameter tuning using Grid Search Cross Validation was implemented although it can also accept user supplied hyperparameter values for SVM, Naïve Bayes, and K-NN models should the user want to do experimentation. Aside from computing for accuracy, the AUPRC and MCC metrics were used to address the dataset imbalance. The results showed that SVM has the best overall performance, obtaining accuracy = 0.7490, AUPRC = 0.5536, and MCC = 0.2924.

Prostate Cancer Classification Based on Histopathological Images

“…Singh et al [29] proposed a cubic SVM classifier for breast cancer detection and classification, achieving a peak accuracy of 92.3%. Additionally, Lopez et al [30] developed a clinical decision support tool incorporating SVM, naïve Bayes, and KNN classifiers, where SVM outperformed others with an accuracy of 0.7490 in detecting invasive ductal carcinoma.…”

SPECT-MPI for Coronary Artery Disease: A Deep Learning Approach

“…13,[15][16][17][18] The visual assessment of myocardial perfusion abnormalities in SPECT-MPI remains an important research challenge to both cardiologists and data scientists since many research studies in clinical medicine now involve applying a variety of machine learning algorithms, deep learning methods, and statistical techniques to improve CAD detection. [19][20][21][22][23] In previous studies involving deep learning methods applied to nuclear medicine imaging, various types of CNN models were applied on bone scans of breast and prostate cancer patients to detect osseous metastasis. 13,[24][25][26][27] Results demonstrated superior performance of these CNN models with several advantages such as high reliability, valid, faster, simpler architecture, and short training even with a comparatively smaller image dataset.…”