Relevant bibliographies by topics / CBIS-DDSM

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Academic literature on the topic 'CBIS-DDSM'

Author: Grafiati
Published: 2 June 2025
Last updated: 19 July 2025

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Journal articles on the topic "CBIS-DDSM"

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Jasim, Hayder Nsaif, Wesam Mohammed Jasim Abid Alrawi, and Mohammed Salah Ibrahim Jassem. "Hyperparameter Optimisation for Breast Cancer Detection Using APO and Pre-Trained CNNs." International Journal of Online and Biomedical Engineering (iJOE) 21, no. 09 (2025): 96–109. https://doi.org/10.3991/ijoe.v21i09.55479.

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Early detection of breast cancer improves survival rates and treatment outcomes. Mammography remains the key diagnostic technique; however, building deep learning models for reliable categorisation is challenging. This paper presents a groundbreaking method for fine-tuning hyperparameters in two cutting-edge convolutional neural networks (CNNs), ConvNeXtBase and ResNet-50, which employ the Arctic Puffin Optimisation (APO) algorithm. Experiments were performed on two benchmark mammography datasets: CBIS-DDSM and MIAS. The APO-optimised ConvNeXtBase model achieved 98.46% accuracy on the CBIS-DDS
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TİRYAKİ, Volkan Müjdat. "Deep Transfer Learning to Classify Mass and Calcification Pathologies from Screen Film Mammograms." Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 12, no. 1 (2023): 57–65. http://dx.doi.org/10.17798/bitlisfen.1190134.

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The number of breast cancer diagnosis is the biggest among all cancers, but it can be treated if diagnosed early. Mammography is commonly used for detecting abnormalities and diagnosing the breast cancer. Breast cancer screening and diagnosis are still being performed by radiologists. In the last decade, deep learning was successfully applied on big image classification databases such as ImageNet. Deep learning methods for the automated breast cancer diagnosis is under investigation. In this study, breast cancer mass and calcification pathologies are classified by using deep transfer learning
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Alkhaleefah, Mohammad, Tan-Hsu Tan, Chuan-Hsun Chang, et al. "Connected-SegNets: A Deep Learning Model for Breast Tumor Segmentation from X-ray Images." Cancers 14, no. 16 (2022): 4030. http://dx.doi.org/10.3390/cancers14164030.

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Inspired by Connected-UNets, this study proposes a deep learning model, called Connected-SegNets, for breast tumor segmentation from X-ray images. In the proposed model, two SegNet architectures are connected with skip connections between their layers. Moreover, the cross-entropy loss function of the original SegNet has been replaced by the intersection over union (IoU) loss function in order to make the proposed model more robust against noise during the training process. As part of data preprocessing, a histogram equalization technique, called contrast limit adapt histogram equalization (CLA
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Thirumalaisamy, Selvakumar, Kamaleshwar Thangavilou, Hariharan Rajadurai, et al. "Breast Cancer Classification Using Synthesized Deep Learning Model with Metaheuristic Optimization Algorithm." Diagnostics 13, no. 18 (2023): 2925. http://dx.doi.org/10.3390/diagnostics13182925.

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Breast cancer is the second leading cause of mortality among women. Early and accurate detection plays a crucial role in lowering its mortality rate. Timely detection and classification of breast cancer enable the most effective treatment. Convolutional neural networks (CNNs) have significantly improved the accuracy of tumor detection and classification in medical imaging compared to traditional methods. This study proposes a comprehensive classification technique for identifying breast cancer, utilizing a synthesized CNN, an enhanced optimization algorithm, and transfer learning. The primary
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Ragab, Dina A., Maha Sharkas, Stephen Marshall, and Jinchang Ren. "Breast cancer detection using deep convolutional neural networks and support vector machines." PeerJ 7 (January 28, 2019): e6201. http://dx.doi.org/10.7717/peerj.6201.

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It is important to detect breast cancer as early as possible. In this manuscript, a new methodology for classifying breast cancer using deep learning and some segmentation techniques are introduced. A new computer aided detection (CAD) system is proposed for classifying benign and malignant mass tumors in breast mammography images. In this CAD system, two segmentation approaches are used. The first approach involves determining the region of interest (ROI) manually, while the second approach uses the technique of threshold and region based. The deep convolutional neural network (DCNN) is used
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Zhang, Qian, Yamei Li, Guohua Zhao, Panpan Man, Yusong Lin, and Meiyun Wang. "A Novel Algorithm for Breast Mass Classification in Digital Mammography Based on Feature Fusion." Journal of Healthcare Engineering 2020 (December 22, 2020): 1–11. http://dx.doi.org/10.1155/2020/8860011.

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Prompt diagnosis of benign and malignant breast masses is essential for early breast cancer screening. Convolutional neural networks (CNNs) can be used to assist in the classification of benign and malignant breast masses. A persistent problem in current mammography mass classification via CNN is the lack of local-invariant features, which cannot effectively respond to geometric image transformations or changes caused by imaging angles. In this study, a novel model that trains both texton representation and deep CNN representation for mass classification tasks is proposed. Rotation-invariant f
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Aliniya, Parvaneh, Mircea Nicolescu, Monica Nicolescu, and George Bebis. "Towards Robust Supervised Pectoral Muscle Segmentation in Mammography Images." Journal of Imaging 10, no. 12 (2024): 331. https://doi.org/10.3390/jimaging10120331.

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Mammography images are the most commonly used tool for breast cancer screening. The presence of pectoral muscle in images for the mediolateral oblique view makes designing a robust automated breast cancer detection system more challenging. Most of the current methods for removing the pectoral muscle are based on traditional machine learning approaches. This is partly due to the lack of segmentation masks of pectoral muscle in available datasets. In this paper, we provide the segmentation masks of the pectoral muscle for the INbreast, MIAS, and CBIS-DDSM datasets, which will enable the developm
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Mohammed, Ahmed Dhahi, and Dursun Ekmekci. "Breast Cancer Diagnosis Using YOLO-Based Multiscale Parallel CNN and Flattened Threshold Swish." Applied Sciences 14, no. 7 (2024): 2680. http://dx.doi.org/10.3390/app14072680.

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In the field of biomedical imaging, the use of Convolutional Neural Networks (CNNs) has achieved impressive success. Additionally, the detection and pathological classification of breast masses creates significant challenges. Traditional mammogram screening, conducted by healthcare professionals, is often exhausting, costly, and prone to errors. To address these issues, this research proposes an end-to-end Computer-Aided Diagnosis (CAD) system utilizing the ‘You Only Look Once’ (YOLO) architecture. The proposed framework begins by enhancing digital mammograms using the Contrast Limited Adaptiv
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Barthwal, Archana, Kapil Joshi, Adarsh Kumar, et al. "Blockchain and Classification of Mammograms and Histopathology Images in Breast Cancer Lesions." Applied and Computational Engineering 8, no. 1 (2023): 436–42. http://dx.doi.org/10.54254/2755-2721/8/20230208.

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This work provides an overview of the research that has been done on using Computer-Aided Detection (CAD) for the diagnosis of breast cancer. The focus is on mammographic and histopathology images and the different techniques used for image pre-processing, segmentation, and classification. The accuracy of the different algorithms was evaluated on different datasets, including MIAS, IRMA, DDSM, and CBIS-DDSM, and the results showed that deep learning models such as Convolutional Neural Networks (CNNs) and Random Forest, along with Multi-Layer Perception and Nave Bayes, were effective in detecti
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Khourdifi, Youness, Alae El Alami, Mounia Zaydi, Yassine Maleh, and Omar Er-Remyly. "Early Breast Cancer Detection Based on Deep Learning: An Ensemble Approach Applied to Mammograms." BioMedInformatics 4, no. 4 (2024): 2338–73. https://doi.org/10.3390/biomedinformatics4040127.

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Background: Breast cancer is one of the leading causes of death in women, making early detection through mammography crucial for improving survival rates. However, human interpretation of mammograms is often prone to diagnostic errors. This study addresses the challenge of improving the accuracy of breast cancer detection by leveraging advanced machine learning techniques. Methods: We propose an extended ensemble deep learning model that integrates three state-of-the-art convolutional neural network (CNN) architectures: VGG16, DenseNet121, and InceptionV3. The model utilizes multi-scale featur
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Book chapters on the topic "CBIS-DDSM"

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Fetić, Ajna, and Nevzudin Buzađija. "Evaluation of U-Net Algorithm Precision for Segmentation and Classification of Breast and Lung Images on CBIS-DDSM and LIDC-IDRI Datasets." In Electronic Health Records - Issues and Challenges in Healthcare Systems [Working Title]. IntechOpen, 2025. https://doi.org/10.5772/intechopen.1007743.

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Cancer is the leading disease in the world in terms of increasing numbers of new patients and deaths annually, making it one of the most formidable diseases today. Lung cancer and breast cancer are widely acknowledged as two of the most prevalent forms of cancer, both falling under the carcinoma subtype. Early detection of these cancers is particularly crucial for patient outcomes. With cancer being a longstanding health challenge, comprehensive datasets containing essential information for cancer prediction and diagnosis are now available. Radiologists primarily rely on Computed Tomography (CT) scans for cancer diagnosis. However, the escalating demand for CT scans and their interpretation has led to radiologists prioritizing throughput over the quality of readings. To address this challenge, Computer-Aided Diagnosis (CAD) systems have been introduced into medical practice. This research paper delves into evaluating the accuracy of the implemented U-Net algorithm for segmentation and classification on two prominent medical datasets: Curated Breast Imaging Subset of DDSM (CBIS-DDSM) and Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The primary objective of the study is to assess the algorithm’s performance in accurately identifying and classifying Regions of Interest (ROIs) in breast and lung medical images.
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Conference papers on the topic "CBIS-DDSM"

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Matos, Ariadne N., and Paulo E. Ambrósio. "Extração de Características de Imagens Mamográficas Baseada em Técnicas de Aprendizado Profundo." In Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação (SBC), 2022. http://dx.doi.org/10.5753/sbcas_estendido.2022.222423.

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Apesar dos constantes avanços no combate ao câncer de mama, ainda é uma das principais causas no óbito de mulheres. A partir do avanço tecnológico, as técnicas de deep learning podem auxiliar na detecção e classificação dos nódulos, o que contribui com o diagnóstico. Apresentamos, neste artigo a proposta e a construção uma rede neural convolucional juntamente com técnicas de regularização denominada MiDNet para extração de características de imagens mamográficas. O modelo foi validado com o conjunto de dados da base CBIS DDSM e Mini DDSM, obtendo índices de acurácia com 98 por cento para difer
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Oliveira, Felipe Victor de Sá, Gersica Agripino Alencar, and Filipe Rolim Cordeiro. "Analysis of Keypoint Detection Algorithms for Mass Candidates Selection in Mammography Images." In XV Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/eniac.2018.4402.

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Breast cancer has been a growing problem for women around the world. The correct interpretation of mammographic images is important for the diagnosis of breast cancer. However, this is a difficult task even for a specialist. Image processing is used to make the diagnosis less susceptible to errors. Thus, the present work proposes a new method for the search of lesion candidates in mammographic images. To verify the efficiency of the approach, the behavior of the SURF, SIFT, BRISK and ORB algorithms were analyzed, as well as the Selective Search algorithm for candidate selection. A total of 121
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Wang, Zhiwei, Junlin Xian, Kangyi Liu, Xin Li, Qiang Li, and Xin Yang. "Dual-view Correlation Hybrid Attention Network for Robust Holistic Mammogram Classification." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/168.

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Mammogram image is important for breast cancer screening, and typically obtained in a dual-view form, i.e., cranio-caudal (CC) and mediolateral oblique (MLO), to provide complementary information for clinical decisions. However, previous methods mostly learn features from the two views independently, which violates the clinical knowledge and ignores the importance of dual-view correlation in the feature learning. In this paper, we propose a dual-view correlation hybrid attention network (DCHA-Net) for robust holistic mammogram classification. Specifically, DCHA-Net is carefully designed to ext
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