Framework of Computer Aided Diagnosis Systems for Cancer Classification Based on Medical Images

Abstract

Early detection of cancer can increase patients' survivability and treatment options. Medical images such as Mammogram, Ultrasound, Magnetic Resonance Imaging, and microscopic images are the common method for cancer diagnosis. Recently, computer-aided diagnosis (CAD) systems have been used to help physicians in cancer diagnosis so that the diagnosis accuracy can be improved. CAD can help in decreasing missed cancer lesions due to physician fatigue, reducing the burden of workload and data overloading, and decreasing variability of inter- and intra-readers of images. In this research, a framework of CAD systems for cancer diagnosis based on medical images has been proposed. The proposed work helps physicians in detection of suspicion regions using different medical images modalities and in classifying the detected suspicious regions as normal or abnormal with the highest possible accuracy. The proposed framework of CAD system consists of four stages which are: preprocessing, segmentation of regions of interest, feature extraction and selection, and finally classification. In this research, the framework has been applied on blood smear images to diagnose the cases as normal or abnormal for Acute Lymphoblastic Leukemia (ALL) cases. Ant Colony Optimization (ACO) has been used to select the subsets of features from the features extracted from segmented cell parts which can maximize the classification performance as possible. Different classifiers which are Decision Tree (DT), K-nearest neighbor (K-NN), Naïve Bayes (NB), and Support Vector Machine (SVM) have been applied. The framework has been yielding promising results which reached 96.25% accuracy, 97.3% sensitivity, and 95.35% specificity using decision tree classifier.

Machine Learning in Detection and Classification of Leukemia Using Smear Blood Images: A Systematic Review

“…To design and develop ML algorithms, hematologists have made some of these datasets (that include PBS images) available to researchers. ALL-IDB, one of the most well-known datasets published in two versions, has been utilized in many articles, most of which have diagnosed and classified acute lymphoblastic leukemia (ALL) via different ML techniques [16][17][18][19][20][21]. ere is another published leukemia dataset called Benchmark for the development of ML algorithms, used by some studies.…”

“…Al-jaboriy et al used the nuclear-to-cytoplasmic ratio, nucleus compactness, nucleus form factors, nucleus eccentricity, nucleus elongation, and nucleus rigidity [17,23,24,27]. Among seven studies, which used traditional ML algorithm, four used the SVM method alone and with other algorithms [18][19][20]24] and three utilized ANN and other algorithms [17]. Note that these algorithms are among the most popular algorithms in medical image processing.…”

Multiplex Immunofluorescence and the Digital Image Analysis Workflow for Evaluation of the Tumor Immune Environment in Translational Research

“…These advances have increased the amount of material and work dedicated to cancer research and diagnosis. Cancer research and diagnosis use medical images such as mammography, magnetic resonance imaging, ultrasound, and microscopic tissue images (1), One of the most actively researched tasks in development is digital image analysis using computer-assisted diagnosis (2)(3)(4)(5)(6). Digital image analysis has become an essential part of cancer research, detection, treatment decisions, and surveillance routines, and it can be potentially used at many screening sites globally (7)(8)(9)(10).…”

“…Digital image analysis has become an essential part of cancer research, detection, treatment decisions, and surveillance routines, and it can be potentially used at many screening sites globally (7)(8)(9)(10). Indeed, this technique has the much-needed potential to relieve pathologists' workloads, diminish subjectivity, improvement of performance, and accuracy, allowing increased attention toward more challenging cases (1,7,10).…”

Developing a Novel Machine Learning-Based Classification Scheme for Predicting SPCs in Breast Cancer Survivors

“…Unlike models for statistical disease prediction, cancer prediction models that are based on machine learning techniques do not require powerful model assumptions and a priori assumptions concerning the properties of the data. They can, however, capture delicate underlying patterns and relationships contained in empirical data and they provide promising cancer prediction results (Tseng et al, 2014; Kourou et al, 2015; Tseng et al, 2017; El Houby, 2018).…”