Fr612 INFLUENCE OF COMPUTER AIDED DIAGNOSIS WITH AUGMENTED VISUALIZATION ON ENDOSCOPIC CLASSIFICATION OF COLORECTAL POLYPS

: Breast Cancer is a common dangerous disease for women. In the world, many women died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues there are several techniques and methods. The image processing, machine learning and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to survive the women's life. To detect the breast masses, microcalcifications, malignant cells the different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have been reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for the survival of women’s life it is essential to improve the methods or techniques to diagnose breast cancer at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.

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Image feature analysis and computer-aided diagnosis in digital radiography: Classification of normal and abnormal lungs with interstitial disease in chest images

Medical Physics ◽

10.1118/1.596412 ◽

1989 ◽

Vol 16 (1) ◽

pp. 38-44 ◽

Cited By ~ 72

Author(s):

Shigehiko Katsuragawa ◽

Kunio Doi ◽

Heber MacMahon

Keyword(s):

Digital Radiography ◽

Computer Aided Diagnosis ◽

Image Feature ◽

Feature Analysis ◽

Computer Aided ◽

Interstitial Disease ◽

Aided Diagnosis

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Convolutional Neural Networks in Computer-Aided Diagnosis of Colorectal Polyps and Cancer: A Review

10.20944/preprints202110.0135.v1 ◽

2021 ◽

Author(s):

Kamyab Keshtkar

Keyword(s):

Colorectal Cancer ◽

Neural Networks ◽

Deep Learning ◽

Computer Aided Diagnosis ◽

Colorectal Polyps ◽

Support Vector ◽

Training Process ◽

Computer Aided ◽

Conventional Models ◽

Aided Diagnosis

As a relatively high percentage of adenoma polyps are missed, a computer-aided diagnosis (CAD) tool based on deep learning can aid the endoscopist in diagnosing colorectal polyps or colorectal cancer in order to decrease polyps missing rate and prevent colorectal cancer mortality. Convolutional Neural Network (CNN) is a deep learning method and has achieved better results in detecting and segmenting specific objects in images in the last decade than conventional models such as regression, support vector machines or artificial neural networks. In recent years, based on the studies in medical imaging criteria, CNN models have acquired promising results in detecting masses and lesions in various body organs, including colorectal polyps. In this review, the structure and architecture of CNN models and how colonoscopy images are processed as input and converted to the output are explained in detail. In most primary studies conducted in the colorectal polyp detection and classification field, the CNN model has been regarded as a black box since the calculations performed at different layers in the model training process have not been clarified precisely. Furthermore, I discuss the differences between the CNN and conventional models, inspect how to train the CNN model for diagnosing colorectal polyps or cancer, and evaluate model performance after the training process.

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Segmentation and Classification of Breast Cancer Tumour

International Journal of Emerging Trends in Health Sciences ◽

10.18844/ijeths.v2i1.3802 ◽

2018 ◽

Vol 2 (1) ◽

pp. 14-18

Author(s):

Gokalp Cinarer ◽

Bulent Gursel Emiroglu ◽

Ahmet Hasim Yurttakal

Keyword(s):

Breast Cancer ◽

Texture Features ◽

Computer Aided Diagnosis ◽

Wavelet Approximation ◽

Proposed Model ◽

Svm Algorithm ◽

Computer Aided ◽

Magnetic Resonance Imaging Mri ◽

Aided Diagnosis

Breast cancer is cancer that forms in the cells of the breasts. Breast cancer is the most common cancer diagnosed in women in the world. Breast cancer can occur in both men and women, but it's far more common in women. Early detection of breast cancer tumours is crucial in the treatment. In this study, we presented a computer aided diagnosis expectation maximization segmentation and co-occurrence texture features from wavelet approximation tumour image of each slice and evaluated the performance of SVM Algorithm. We tested the model on 50 patients, among them, 25 are benign and 25 malign. The 80% of the images are allocated for training and 20% of images reserved for testing. The proposed model classified 2 patients correctly with success rate of 80% in case of 5 Fold Cross-Validation Keywords: Breast Cancer, Computer-Aided Diagnosis (CAD), Magnetic Resonance Imaging (MRI);

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