Computer-aided diagnosis for early detection and staging of human pancreatic tumors using an optimized 3D CNN on computed tomography

Vishnudas, Chaithanyadas Kanady; Gnana King, G. R.

doi:10.1007/s00530-023-01146-2

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2024

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Cited by 2 publications

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Ensemble Neural Network 3D-CNN and LSTM in the Problem of Assessing the State of a Technological System for Processing Ore Waste

Puchkov,

Dli,

Bobkov

2024

2024 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

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Ensemble Neural Network 3D-CNN and LSTM in the Problem of Assessing the State of a Technological System for Processing Ore Waste

Puchkov,

Dli,

Bobkov

2024

2024 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

View full text Add to dashboard Cite

Enhanced Detection and Segmentation of Brain Tumors Using a Dense BW‐CNN Approach

Kakarwal,

Mapari,

Jain

et al. 2024

Journal of Engineering

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This study introduces a novel Black Widow‐Optimized Dense CNN (BW‐DCNN) framework for the automated detection and segmentation of brain tumors in MRI scans, aiming to enhance early diagnosis and treatment planning. With the increasing frequency of brain tumors, early and accurate detection becomes crucial for effective treatment. Magnetic Resonance Imaging (MRI) is a diagnostic technique owing to its excellent sensitivity. However, manual MRI data analysis is time‐consuming and prone to errors. Leveraging advancements in Convolutional Neural Networks (CNNs) and optimization algorithms, the proposed BW‐DCNN framework utilizes a unique integration of preprocessing, segmentation enhancement, and classification techniques optimized through Black Widow Optimization to improve diagnostic accuracy and efficiency. Evaluating the BW‐DCNN framework against existing methodologies, including DCNN, DNN, and DBN, demonstrates superior performance across a comprehensive suite of metrics. These results highlight the potential of the BW‐DCNN approach to significantly advance the capabilities of computer‐aided diagnostic systems in medical imaging, offering a promising direction for future research and application in clinical settings.

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Computer-aided diagnosis for early detection and staging of human pancreatic tumors using an optimized 3D CNN on computed tomography

Cited by 2 publications

References 40 publications

Ensemble Neural Network 3D-CNN and LSTM in the Problem of Assessing the State of a Technological System for Processing Ore Waste

Ensemble Neural Network 3D-CNN and LSTM in the Problem of Assessing the State of a Technological System for Processing Ore Waste

Enhanced Detection and Segmentation of Brain Tumors Using a Dense BW‐CNN Approach

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