Evaluation of Deep Learning Models for Detecting Breast Cancer Using Mammograms

Mohapatra, Subasish; Muduly, Sarmistha; Mohanty, Subhadarshini; Moharana, Santosh Kumar

doi:10.1007/978-3-031-11713-8_11

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…The foal and stallion can be selected from the preliminary population for producing several groups. An overview of CNN models used to detect mammograms for benign, cancerous, or normal tumors is provided in this article [39].…”

Section: Design Of Ewho Algorithm For Hyperparameter Optimizationmentioning

confidence: 99%

Long Short-Term Memory-Deep Belief Network-Based Gene Expression Data Analysis for Prostate Cancer Detection and Classification

Sethi,

Singh,

Rout

et al. 2024

IEEE Access

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Prostate cancer (PRC) is the major reason of mortality globally. Early recognition and classification of PRC become essential to enhance the quality of healthcare services. A newly established deep learning (DL) and machine learning (ML) approach with different optimization tools can be employed to classify accurately of PRC accurately using microarray gene expression data (GED). Though the microarray data structures are important to diagnosing different kinds of diseases, the optimum hyperparameter tuning of the DL models poses a major challenge to achieving maximum classification performance. To resolve these issues, this study develops a new Gene Expression Data Analysis using Artificial Intelligence for Prostate Cancer Diagnoses (GEDAAI-PCD) technique. The proposed GEDAAI-PCD technique examines the GED for the identification of PRC. To accomplish this, the GEDAAI-PCD technique initially normalizes the GED into a uniform format. In addition, the long short-term memory-deep belief network (LSTM-DBN) model was applied for PRC classification purposes. The wild horse optimization (EWHO) system was utilized as a hyperparameter tuning strategy to optimize the performance of the LSTM-DBN model. The experimental assessment of the GEDAAI-PCD system occurs on open openaccessed gene expression database. The experimental outcomes emphasized the supremacy of the GEDAAI-PCD method on PRC classification.

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Section: Design Of Ewho Algorithm For Hyperparameter Optimizationmentioning

confidence: 99%

Long Short-Term Memory-Deep Belief Network-Based Gene Expression Data Analysis for Prostate Cancer Detection and Classification

Sethi,

Singh,

Rout

et al. 2024

IEEE Access

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“…The Mini-DDSM dataset is not as extensively utilized in published studies as the CBIS-DDSM. [24][25][26] This discrepancy can be attributed to the fact that CBIS-DDSM encompasses a more diverse range of information for each mammogram image as the Mini-DDSM doesn't contain information like the abnormality type, BI-RADS assessment, subtlety score, mass shape and margin, and calcification type and distribution (Supplementary table 4).…”

Section: Mini-ddsmmentioning

confidence: 99%

From Data to Diagnosis: Narrative Review of Open-Access Mammography Databases for Breast Cancer Detection

Jaradat,

Amro,

Hamamreh

et al. 2024

HYMR

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Breast cancer remains a significant global health challenge, necessitating advancements in screening and diagnostic methods for its early detection and treatment. This review explores the role of open-access mammography databases in facilitating research and development in the field of breast cancer detection, particularly through the integration of artificial intelligence techniques such as machine learning and deep learning. In this review, we highlight the open-access databases, including the Digital Database for Screening Mammography (DDSM), the Curated Breast Imaging Subset of DDSM (CBIS-DDSM), Mini-DDSM, INbreast, Mammographic Image Analysis Society Dataset (MIAS), and the China Mammography and Mastopathy Dataset (CMMD). Each database was analyzed in terms of its composition, features, limitations, and contributions to breast cancer research. In addition, we highlight the importance of open-access databases in enabling collaborative research, improving algorithm development, and enhancing the accuracy and efficiency of breast cancer detection methods computer-aided diagnosis.

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“…Various deep learning models, including convolutional neural networks (CNNs), have been used in many studies to categorize mammograms into benign or malignant tumors and find problematic areas of interest (ROIs) in mammograms [1]. The breast dataset and the Digital Database for Screening Mammography (CBIS-DDSM) are two examples of publicly accessible datasets used for research for model training and assessment [2].…”

Section: Introductionmentioning

confidence: 99%

Classification of Breast Cancer Using Radiological Society of North America Data by EfficientNet

Huynh,

Nguyen,

Tran

et al. 2023

2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability

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Evaluation of Deep Learning Models for Detecting Breast Cancer Using Mammograms

Cited by 3 publications

References 16 publications

Long Short-Term Memory-Deep Belief Network-Based Gene Expression Data Analysis for Prostate Cancer Detection and Classification

Long Short-Term Memory-Deep Belief Network-Based Gene Expression Data Analysis for Prostate Cancer Detection and Classification

From Data to Diagnosis: Narrative Review of Open-Access Mammography Databases for Breast Cancer Detection

Classification of Breast Cancer Using Radiological Society of North America Data by EfficientNet

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