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Traditional machine learning methods have been replaced by deep learning (DL), a modern, cutting-edge approach to classifying textures and localizing tissues. This study introduces a dataset for texture classification at the image level using integrated transfer learning-based EfficientNet-B7 deep convolutional neural networks (CNN). The dataset comprised 381 images of dimensions 150 × 150 pixels for training, validation, and testing. The model attained an accuracy of 89.33%, 52.43%, and, 51.326% for training, validation, and testing datasets while training losses are 0.2513, 1.846, and 1.6137, respectively. The straightforwardness of the framework, setup, and execution also plays a role in the findings of this research. Moreover, these proposed techniques can suggest extracting more prognostic information than an experienced human observer.

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MLCDL: A Critical Practice and Implementation of Multi-tissue Classification and Diagnosis Using Deep Learning Algorithm

  • 实验方案

  • 发表日期：

    2025年

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  • Pijush Dutta,
  • Amit Dey,
  • Raushan Das,
  • Raghunath Maji,
  • Shobhondeb Paul,
  • Sudip Mandal

摘要

Traditional machine learning methods have been replaced by deep learning (DL), a modern, cutting-edge approach to classifying textures and localizing tissues. This study introduces a dataset for texture classification at the image level using integrated transfer learning-based EfficientNet-B7 deep convolutional neural networks (CNN). The dataset comprised 381 images of dimensions 150 × 150 pixels for training, validation, and testing. The model attained an accuracy of 89.33%, 52.43%, and, 51.326% for training, validation, and testing datasets while training losses are 0.2513, 1.846, and 1.6137, respectively. The straightforwardness of the framework, setup, and execution also plays a role in the findings of this research. Moreover, these proposed techniques can suggest extracting more prognostic information than an experienced human observer.