Subjectbreast tumors , mammograms , shape factors , malignant breast tumors , benign breast tumors , shape complexity , radiographic definition , compactness , moments , Fourier descriptors , chord length statistics , spiculated mass , circumscribed mass , four-group classification , breast cancer diagnosis
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AbstractDistinguishing between benign and malignant breast tumors requires careful analysis of their shape complexity and radiographic definition. In this paper we examine the usefulness of shape factors such as compactness, moments, Fourier descriptors, and statistics of chord lengths in distinguishing between circumscribed/spiculated and benign/malignant masses. A database of 54 tumors was used in pattern classification experiments. Classification accuracies of 95% for circumscribed/spiculated, 76% for benign/malignant, and 77% for four-group classification were obtained, which indicate the usefulness of the proposed methods in breast cancer diagnosis.
DepartmentElectrical and Computer Engineering
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Artificial Intelligence-Based Breast Cancer DiagnosisUsing Ultrasound Images and Grid-Based DeepFeature GeneratorLiu, Haixia; Cui, Guozhong; Luo, Yi; Guo, Yajie; Zhao, Lianli; Wang, Yueheng; Subasi, Abdulhamit; External Collaboration; Artificial Intelligence & Cyber Security Lab; Computer Science; et al. (Taylor & Francis, 2022-11-14)Purpose:Breast cancer is a prominent cancer type with high mortality. Early detection of breast cancer could serve to improve clinical outcomes. Ultrasonography is a digital imaging technique used to differentiate benign and malignant tumors. Several artificial intelligence techniques have been suggested in the literature for breast cancer detection using breast ultrasonography (BUS). Nowadays, particularly deep learning methods have been applied to biomedical images to achieve high classification performances.Patients and Methods:This work presents a new deep feature generation technique for breast cancer detection using BUS images.The widely known 16 pre-trained CNN models have been used in this framework as feature generators. In the feature generationphase, the used input image is divided into rows and columns, and these deep feature generators (pre-trained models) have applied to each row and column. Therefore, this method is called a grid-based deep feature generator. The proposed grid-based deep feature generator can calculate the error value of each deep feature generator, and then it selects the best three feature vectors as a final feature vector. In the feature selection phase, iterative neighborhood component analysis (INCA) chooses 980 features as an optimal number of features. Finally, these features are classified by using a deep neural network (DNN). Results:The developed grid-based deep feature generation-based image classification model reached 97.18% classification accuracy on the ultrasonic images for three classes, namely malignant, benign, and normal.Conclusion:The findings obviously denoted that the proposed grid deep feature generator and INCA-based feature selection model successfully classified breast ultrasonic images.
Diagnosis of breast cancer from histopathological images with deep learning architecturesHancer, Emrah; Subasi, Abdulhamit; External Collaboration; Artificial Intelligence & Cyber Security Lab; Computer Science; Hancer, Emrah (Academic Press, 2023-01-20)Breast cancer is one of the most common cancer types among women worldwide. If not treated in earlier stages, it may be fatal. Therefore early diagnosis of breast cancer can minimize the human life risk. Mammograms and ultrasound imaging technologies play a crucial role to detect intraductal papillomas. However, the determination process of intraductal papillomas requires histopathological image analysis which may be mostly time-consuming, subjective, and tedious if carried out manually by the experts. To cover issue, computer-aided diagnosis (CAD) systems came into consideration. However, earlier CAD systems could not achieve significant improvement in the diagnosis process and their usage of them did not become widespread for more than a decade. Since deep learning has made so many significant advances in a wide variety of image applications, CAD systems that use its principles perform as well as the experts in stand-alone mode, and even perform better when used in support mode. In this chapter, we utilized various deep learning architectures for the detection process of breast cancer on the invasive ductal carcinoma (IDC) dataset which is one of the most popular and remarkable datasets in this field. According to the results, the pretrained VGG16 and MobileNet architectures obtain the best detection performance, reaching nearly 92% classification accuracy.
Breast tumor detection in ultrasound images using artificial intelligenceModi, Omkar; Subasi, Abdulhamit; External Collaboration; Artificial Intelligence & Cyber Security Lab; Computer Science; Omkar, Modi (Academic Press, 2023-01-20)Leveraging artificial intelligence (AI) for categorizing breast tumors as malignant or benign from breast ultrasound images can provide an effective and relatively low-cost method for the diagnosis of breast cancer. Presently, many machine learning (ML) and deep learning (DL) algorithms have been used for early-stage breast cancer detection. AI algorithms have shown promising results in breast cancer detection tasks. The use of deep convolutional neural network approaches has provided solutions for the efficient analysis of breast ultrasound images. Convolutional neural network (CNN) models analyze the image data in multiple layers and extract features which helps in better feature extractions and better performance in comparison to the conventional ML algorithms. Apart from conventional learning algorithms, we use the transfer learning technique which uses knowledge from its previous training in another related problem set. In this chapter, we have demonstrated the use of DL models through transfer learning, deep feature extraction, machine learning models, and comparison of their performances.