Classification of mammograms using adaptive binary TLBO with ensemble classifier for early detection of breast cancer

Kumari, Lalitha; Jagadesh, B. N.

doi:10.1007/s41870-022-00998-7

Cited by 9 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both [10] and [11] investigate CNNs' potential to be used in the diagnosis of breast cancer. A novel metaheuristic algorithm-based machine learning model and Fuzzy C Means-based segmentation technique for the classification and detection of breast cancer from mammogram images of [12] The integration and selection of deep features are also the subject of [13]. Convolutional neural networks (CNNs) and transfer learning were shown in this research to achieve very high accuracy in histopathology image classification of breast cancer.…”

Section: A Optimization Techniques In Enhancing Model Performancementioning

confidence: 95%

ACNGCNN: Improving Efficiency of Breast Cancer Detection and Progression using Adversarial Capsule Network with Graph Convolutional Neural Networks

Pallapu,

Syed

2024

ijacsa

View full text Add to dashboard Cite

New diagnostic methods are needed to improve the accuracy and efficiency of breast cancer detection and progression. Although successful, current methods frequently lack precision, accuracy, and timeliness, especially in the early phases Of breast cancer progression. Our research proposes a new model using deep learning to improve breast cancer detection and classification, addressing constraints. Our breast cancer image and sample preprocessing approach combines a nonlocal means filter (NLM) and Generative Adversarial Networks (GAN). The model classifies datasets using LSTM with BiGRUbased Recurrent ShuffleNet V2, a highly efficient and accurate technique for sequential data samples. The integration of a Capsule Network with Graph Convolutional Neural Networks (CNGCNN) significantly improves breast cancer detection. This method was carefully tested on BreaKHis. The results were amazing, showing gains across multiple metrics: 4.9% greater precision, 3.5% higher accuracy, 3.4% higher recall, 2.5% higher AUC (Area Under the Curve), 1.9% higher specificity, and 3.4% decreased delay in the identification of breast cancer stages. Particularly striking was the model's performance in diagnosing illness development, where it displayed 3.5% greater precision, 3.9% higher accuracy, 4.5% higher recall, 3.4% higher AUC, 2.9% higher specificity, and 1.5% lower latency. Significant clinical impacts result from this work. Our methodology enables early diagnosis and precise staging of breast cancer, enabling focused therapies to improve patient outcomes and survival rates. The greater precision and reduced time lag in diagnosing disease progression also allow for more effective monitoring and treatment modifications. Overall, this study marks a considerable improvement in the field of breast cancer diagnostics, delivering a more efficient, accurate, and reliable tool for healthcare providers in their fight against this ubiquitous disease.

show abstract