Detection and Classification of Brain Tumor and Breast Cancer by Using an Efficient Method Based on Image Processing and Fuzzy Inference System
Abstract
Brain tumor and breast cancer are considered to be the most fatal cases for the health of people in modern days. Therefore, early and precise detection of these cases can save many lives all over the world. However, detection and classification of the tumor/cancer area precisely can help the doctors for diagnosing and treatment. In this article, a method based on different noise removal and image adjustment techniques integrated with a Mamdani Fuzzy Inference System (FIS) is proposed to efficiently detect and classify both brain tumor and breast cancer. Firstly, this method is used for detection and classification of brain tumor from the standard Magnetic Resonance Imaging (MRI) dataset. Then, the same method is utilized to detect and classify breast cancer from standard Mammography image dataset with a little modification of the inputs and outputs of the FIS. The use of Otsu’s Method for Global Image Thresholding of the intensity adjusted image and the Robert’s Method for edge detection of the cancer/tumor area increase the efficiency of the method. Furthermore, the performance of this technique is compared with the other conventional neural network and fuzzy based techniques. Accordingly, it is found that this method is competitive with them on various performance parameters.
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Test data available: https://github.com/ferasbg/glioAI.
Database available: https://figshare.com/articles/dataset/brain_tumor_dataset/1512427.
Dataset available: https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection.
Test data available: https://github.com/st186/Detection-of-Breast-Cancer-using-Neural-Networks.
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