Binary and Multiclass Classification of Histopathological Images Using Machine Learning Techniques
Background and Objective: Breast cancer is fairly common and widespread form of cancer among women. Digital mammogram, thermal images of breast and digital histopathological images serve as a major tool for the diagnosis and grading of cancer. In this paper, a novel attempt has
been proposed using image analysis and machine learning algorithm to develop an automated system for the diagnosis and grading of cancer. Methods: BreaKHis dataset is employed for the present work where images are available with different magnification factor namely 40×, 100×,
200×, 400× and 200× magnification factor is utilized for the present work. Accurate preprocessing steps and precise segmentation of nuclei in histopathology image is a necessary prerequisite for building an automated system. In this work, 103 images from benign and 103 malignant
images are used. Initially color image is reshaped to gray scale format by applying Otsu thresholding, followed by top hat, bottom hat transform in preprocessing stage. The threshold value selected based on Ridler and calvard algorithm, extended minima transform and median filtering is applied
for doing further steps in preprocessing. For segmentation of nuclei distance transform and watershed are used. Finally, for feature extraction, two different methods are explored. Result: In binary classification benign and malignant classification is done with the highest accuracy
rate of 89.7% using ensemble bagged tree classifier. In case of multiclass classification 5-class are taken which are adenosis, fibro adenoma, tubular adenoma, mucinous carcinoma and papillary carcinoma the combination of multiclass classification gives the accuracy of 88.1% using ensemble
subspace discriminant classifier. To the best of author’s knowledge, it is the first made in a novel attempt made for binary and multiclass classification of histopathology images. Conclusion: By using ensemble bagged tree and ensemble subspace discriminant classifiers the proposed
method is efficient and outperform the state of art method in the literature.
Keywords: Extended Minima Transform; GLCM (Gray Level Co-Occurrence Matrix); Gabor Filter; Median Filter; Ridler and Calvard Algorithm; Watershed
Document Type: Research Article
Affiliations: 1: Zhejiang Industry Polytechnic College, Shaoxing City Zhejiang Province, 312000, China 2: Waltonchain Blockchain Institute (Non-Profit Consortium), Seoul, 06651, South Korea 3: School of Electrical Electronics Engineering, Department of Electronics and Communication Engineering, SASTRA Deemed University, Thanjavur 613403, Tamilnadu, India
Publication date: 01 August 2020
- Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
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