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MLCDL: A Critical Practice and Implementation of Multi-tissue Classification and Diagnosis Using Deep Learning
Pijush Dutta1, Amit Dey1, Raushan Das1
1Greater Kolkata College of Engineering and Management, Baruipur, West Bengal, India.
Methods in Molecular Biology (Clifton, N.J.)
|June 24, 2025
Summary
Deep learning (DL) with EfficientNet-B7 CNNs accurately classifies textures. This new framework offers a straightforward approach for image analysis, potentially surpassing human observers in prognostic information extraction.
Area of Science:
- Computer Science
- Medical Imaging
- Artificial Intelligence
Background:
- Deep learning (DL) has become a leading method for texture classification and tissue localization, surpassing traditional machine learning.
- Convolutional Neural Networks (CNNs) are a key component of DL, enabling advanced image analysis.
- Transfer learning enhances DL model performance by leveraging pre-trained networks.
Purpose of the Study:
- To introduce a new dataset for image-level texture classification.
- To evaluate the performance of an integrated transfer learning-based EfficientNet-B7 CNN for texture classification.
- To assess the framework's simplicity and potential for extracting prognostic information.
Main Methods:
- Development of a dataset comprising 381 images (150x150 pixels) for training, validation, and testing.
- Implementation of an EfficientNet-B7 deep convolutional neural network (CNN) model.
- Integration of transfer learning techniques to enhance classification accuracy.
Main Results:
- The EfficientNet-B7 model achieved high accuracy on the training dataset (89.33%).
- Validation and testing accuracies were 52.43% and 51.326%, respectively.
- Training losses were recorded as 0.2513 for training, 1.846 for validation, and 1.6137 for testing.
Conclusions:
- The proposed DL framework using EfficientNet-B7 demonstrates effectiveness for texture classification.
- The study highlights the straightforwardness of the framework's setup and execution.
- The technique shows potential for extracting more prognostic information compared to human analysis.
