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The assistance of deep learning for medical image diagnosis is often crucial in the timely treatment of patients suffering from diseases like brain tumors and lung cancer. This paper evaluates the performance of VGG 16 and Efficient-Net deep learning models for the classification of MRI and CT scans of the brain and lungs. A new low complex algorithm referred to as PDBS was promoted to increase the efficiency of model optimizations. The lesion detection models were assessed for accuracy, training time, and level of generalization attained. Experimental results highlight that the PDBS model consistently outperformed traditional CNN architectures, achieving higher classification accuracy with 97% testing accuracy for brain MRI scans and 96.5% for lung CT scans while maintaining efficiency. These results above illustrate the depth of the contribution offered by deep learning methods to the enhancement of medical image analysis to support clinical workflow.

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Classification of Brain and Lungs Images Using Deep Learning Models and Low Complexity Algorithm

  • 会议论文

  • 发表日期：

    2026年

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  • Tejas Nadagadalli,
  • Vishwanath Baligar

摘要

The assistance of deep learning for medical image diagnosis is often crucial in the timely treatment of patients suffering from diseases like brain tumors and lung cancer. This paper evaluates the performance of VGG 16 and Efficient-Net deep learning models for the classification of MRI and CT scans of the brain and lungs. A new low complex algorithm referred to as PDBS was promoted to increase the efficiency of model optimizations. The lesion detection models were assessed for accuracy, training time, and level of generalization attained. Experimental results highlight that the PDBS model consistently outperformed traditional CNN architectures, achieving higher classification accuracy with 97% testing accuracy for brain MRI scans and 96.5% for lung CT scans while maintaining efficiency. These results above illustrate the depth of the contribution offered by deep learning methods to the enhancement of medical image analysis to support clinical workflow.