A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images

Balasubramaniam, Sathiyabhama; Velmurugan, Yuvarajan; Jaganathan, Dhayanithi; Dhanasekaran, Seshathiri

doi:10.3390/diagnostics13172746

Cited by 27 publications

(8 citation statements)

References 49 publications

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“…Balasubramaniam et al [ 24 ] have designed a Modified LeNet architecture (type of CNN) which has been successfully applied to breast cancer data analysis. It demonstrated its ability to extract discriminative features and classify malignant and benign tumors with high accuracy, thereby supporting early detection and diagnosis of breast cancer.…”

Section: Literature Surveymentioning

confidence: 99%

“…The designed classifier is evaluated against the benchmarking deep learning models, proving that this has produced a higher recognition rate. The accuracy of the breast image recognition rate was 89.91% and achieved better performance in breast cancer tumor detection [ 24 ].…”

Section: Literature Surveymentioning

confidence: 99%

“…CNNs have shown promising results in automating pneumonia detection from chest X-ray images. LeNet, a classic CNN architecture, has been used in various image classification tasks [ 24 ]. In this study, we propose a concatenated modified LeNet classifier that leverages the power of deep learning to accurately classify pneumonia images.…”

Section: Introductionmentioning

confidence: 99%

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Concatenated Modified LeNet Approach for Classifying Pneumonia Images

Jaganathan,

Balsubramaniam,

Sureshkumar

et al. 2024

JPM

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Pneumonia remains a critical health concern worldwide, necessitating efficient diagnostic tools to enhance patient care. This research proposes a concatenated modified LeNet classifier to classify pneumonia images accurately. The model leverages deep learning techniques to improve the diagnosis of Pneumonia, leading to more effective and timely treatment. Our modified LeNet architecture incorporates a revised Rectified Linear Unit (ReLU) activation function. This enhancement aims to boost the discriminative capacity of the features learned by the model. Furthermore, we integrate batch normalization to stabilize the training process and enhance performance within smaller, less complex, CNN architectures like LeNet. Batch normalization addresses internal covariate shift, a phenomenon where the distribution of activations within a network alter during training. These modifications help to prevent overfitting and decrease computational time. A comprehensive dataset is used to evaluate the model’s performance, and the model is benchmarked against relevant deep-learning models. The results demonstrate a high recognition rate, with an accuracy of 96% in pneumonia image recognition. This research suggests that the Concatenated Modified LeNet classifier has the potential to be a highly useful tool for medical professionals in the diagnosis of pneumonia. By offering accurate and efficient image classification, our model could contribute to improved treatment decisions and patient outcomes.

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Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

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Concatenated Modified LeNet Approach for Classifying Pneumonia Images

Jaganathan,

Balsubramaniam,

Sureshkumar

et al. 2024

JPM

Self Cite

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“…Breast cancer data were successfully analyzed in [87] using LeNet, a classic CNN architecture. The system demonstrated high accuracy in early detection and diagnosis of breast cancer by extracting discriminative features and classifying malignant and benign tumors.…”

Section: Feature Extractionmentioning

confidence: 99%

A Review of Artificial Intelligence in Breast Imaging

Al-Karawi,

Al-Zaidi,

Helael

et al. 2024

Tomography

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With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging, and prognosis. Breast cancer is a common malignancy that critically affects women’s physical and mental health. Early breast cancer screening—through mammography, ultrasound, or magnetic resonance imaging (MRI)—can substantially improve the prognosis for breast cancer patients. AI applications have shown excellent performance in various image recognition tasks, and their use in breast cancer screening has been explored in numerous studies. This paper introduces relevant AI techniques and their applications in the field of medical imaging of the breast (mammography and ultrasound), specifically in terms of identifying, segmenting, and classifying lesions; assessing breast cancer risk; and improving image quality. Focusing on medical imaging for breast cancer, this paper also reviews related challenges and prospects for AI.

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“…In the research conducted by Sathiyabhama et al, a LeNet convolutional neural network (CNN) model was specifically crafted for the analysis of breast cancer data [35]. This model showed impressive accuracy in differentiating between benign and malignant tumors and successfully extracted discriminative features.…”

Section: Literature Reviewmentioning

confidence: 99%

Revolutionizing Breast Cancer Diagnosis: A Concatenated Precision through Transfer Learning in Histopathological Data Analysis

Jaganathan,

Balasubramaniam,

Sureshkumar

et al. 2024

Diagnostics

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Breast cancer remains a significant global public health concern, emphasizing the critical role of accurate histopathological analysis in diagnosis and treatment planning. In recent years, the advent of deep learning techniques has showcased notable potential in elevating the precision and efficiency of histopathological data analysis. The proposed work introduces a novel approach that harnesses the power of Transfer Learning to capitalize on knowledge gleaned from pre-trained models, adapting it to the nuanced landscape of breast cancer histopathology. Our proposed model, a Transfer Learning-based concatenated model, exhibits substantial performance enhancements compared to traditional methodologies. Leveraging well-established pretrained models such as VGG-16, MobileNetV2, ResNet50, and DenseNet121—each Convolutional Neural Network architecture designed for classification tasks—this study meticulously tunes hyperparameters to optimize model performance. The implementation of a concatenated classification model is systematically benchmarked against individual classifiers on histopathological data. Remarkably, our concatenated model achieves an impressive training accuracy of 98%. The outcomes of our experiments underscore the efficacy of this four-level concatenated model in advancing the accuracy of breast cancer histopathological data analysis. By synergizing the strengths of deep learning and transfer learning, our approach holds the potential to augment the diagnostic capabilities of pathologists, thereby contributing to more informed and personalized treatment planning for individuals diagnosed with breast cancer. This research heralds a promising stride toward leveraging cutting-edge technology to refine the understanding and management of breast cancer, marking a significant advancement in the intersection of artificial intelligence and healthcare.

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A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images

Cited by 27 publications

References 49 publications

Concatenated Modified LeNet Approach for Classifying Pneumonia Images

Concatenated Modified LeNet Approach for Classifying Pneumonia Images

A Review of Artificial Intelligence in Breast Imaging

Revolutionizing Breast Cancer Diagnosis: A Concatenated Precision through Transfer Learning in Histopathological Data Analysis

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