Evaluating Diagnostic Performance of Machine Learning Algorithms on Breast Cancer

Gatuha, George; Jiang, Tao

doi:10.1007/978-3-319-23862-3_25

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…A set of Mammogram images has been classified by Angayarkanni et al [8], and they achieved 99.50% accuracy using the Gray-Level-Cooccurence Matrix (GLCM) as feature. Gatuha et al [9] utilized Mammogram images for image classification using a total of 11 features and achieved 97.30% accuracy. Breast Histopathological images have been classified by Zhang et al [10] and they achieved 95.22% accuracy, where they utilized the Curvelet Transform, GLCM, and Completed Local Binary Pattern (CLBP) methods for feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network

Nahid

Kong

2018

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Identification of the malignancy of tissues from Histopathological images has always been an issue of concern to doctors and radiologists. This task is time-consuming, tedious and moreover very challenging. Success in finding malignancy from Histopathological images primarily depends on long-term experience, though sometimes experts disagree on their decisions. However, Computer Aided Diagnosis (CAD) techniques help the radiologist to give a second opinion that can increase the reliability of the radiologist's decision. Among the different image analysis techniques, classification of the images has always been a challenging task. Due to the intense complexity of biomedical images, it is always very challenging to provide a reliable decision about an image. The state-of-the-art Convolutional Neural Network (CNN) technique has had great success in natural image classification. Utilizing advanced engineering techniques along with the CNN, in this paper, we have classified a set of Histopathological Breast-Cancer (BC) images utilizing a state-of-the-art CNN model containing a residual block. Conventional CNN operation takes raw images as input and extracts the global features; however, the object oriented local features also contain significant information-for example, the Local Binary Pattern (

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Section: Introductionmentioning

confidence: 99%

Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network

Nahid

Kong

2018

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

Houby

2018

J Med Syst

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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.

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Detection and Diagnosis of Breast Cancer Using Deep Learning

Alahe

Maniruzzaman

2021

2021 IEEE Region 10 Symposium (TENSYMP)

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Evaluating Diagnostic Performance of Machine Learning Algorithms on Breast Cancer

Cited by 3 publications

References 7 publications

Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network

Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network

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

Detection and Diagnosis of Breast Cancer Using Deep Learning

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