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<p>Sorting colorectal histology images helps physicians diagnose then cure. Deep CNNs excel in digital pathology. However, their high-dimensional feature spaces cause overfitting, interpretability issues, and decreased generalization across datasets with different staining methods than glandular structures. The hybrid system TL–LASSO-Net combines transfer learning-driven deep feature extraction with LASSO regression for better sparse feature selection. To get high-level features from pre-trained backbones (ResNet50, DenseNet121, and ViT-B/16 for comparison) and compress them using logistic LASSO. After that, a lightweight dense classifier works in a smaller, more discriminative subspace. Two benchmark histopathology datasets, LC25000 and GlaS, are used for the experiments. TL–LASSO-Net gets 98.3% accuracy and ROC–AUC of 0.997 on LC25000, which is better than ResNet50-TL (97.5%, AUC 0.993) and DenseNet121-TL (97.3%, AUC 0.992). The suggested method gets 97.2% accuracy and 0.994 ROC–AUC on GlaS, which is better than all the other methods. Cross-dataset evaluations (LC25000→GlaS and GlaS→LC25000) further show that generalization has gotten better. TL–LASSO-Net has an accuracy of up to 91.3%, whereas the best baseline has an accuracy of just 88.9%. The results show that combining transfer learning with LASSO-driven sparsity makes a model that is small, easy to understand, and quick to compute that can be used to help computers diagnose colorectal cancer.</p>

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TL–LASSO-Net: a hybrid transfer learning and LASSO-based framework for robust colon cancer histopathology classification on LC25000 and GlaS datasets

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月9日

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  • Ladly Patel,
  • Venkatanareshbabu Kuppili,
  • E. Naresh

摘要

Sorting colorectal histology images helps physicians diagnose then cure. Deep CNNs excel in digital pathology. However, their high-dimensional feature spaces cause overfitting, interpretability issues, and decreased generalization across datasets with different staining methods than glandular structures. The hybrid system TL–LASSO-Net combines transfer learning-driven deep feature extraction with LASSO regression for better sparse feature selection. To get high-level features from pre-trained backbones (ResNet50, DenseNet121, and ViT-B/16 for comparison) and compress them using logistic LASSO. After that, a lightweight dense classifier works in a smaller, more discriminative subspace. Two benchmark histopathology datasets, LC25000 and GlaS, are used for the experiments. TL–LASSO-Net gets 98.3% accuracy and ROC–AUC of 0.997 on LC25000, which is better than ResNet50-TL (97.5%, AUC 0.993) and DenseNet121-TL (97.3%, AUC 0.992). The suggested method gets 97.2% accuracy and 0.994 ROC–AUC on GlaS, which is better than all the other methods. Cross-dataset evaluations (LC25000→GlaS and GlaS→LC25000) further show that generalization has gotten better. TL–LASSO-Net has an accuracy of up to 91.3%, whereas the best baseline has an accuracy of just 88.9%. The results show that combining transfer learning with LASSO-driven sparsity makes a model that is small, easy to understand, and quick to compute that can be used to help computers diagnose colorectal cancer.