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January 1, 2026International Journal of Biomedical Engineering and Technology

Leakage-Safe Case-Level Evaluation of ResNet50V2 for Benign-Malignant Liver Tumour Classification on CTSlices

Smitha B Vinod Kumar Kumar S. S

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Key Points

The aim is to evaluate the effectiveness of ResNet50V2 in classifying liver tumours as benign or malignant using CT images.
Employed ResNet50V2 for liver tumour classification on computed tomography (CT) slices.
Applied evaluation metrics to assess performance accuracy of the model.
Conducted leakage-safe case-level analysis to ensure robust evaluation.
Achieved high accuracy in distinguishing between benign and malignant liver tumours.
Demonstrated strong reliability and reduced chances of leakage in classification outcomes.

Implication

Evaluates tumour classification accuracy of ResNet50V2 on CT slices, suggesting improved diagnostic methods.

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Cite This Study

B et al. (2026) studied this question.

synapsesocial.com/papers/6a06b888e7dec685947ab064 https://doi.org/10.1504/ijbet.2026.10078321

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Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Leveraging deep learning and explainable AI for effective liver tumor classification from CT scan imagesOriginal Research · 2026
2Reliable Benchmarking of Breast Ultrasound Lesion Classification Requires Patient-Level ValidationOriginal Research · 2026
3Automatic segmentation of liver tumors from computed tomographic images using hybrid deep learning modelOriginal Research · 2026
4SECT-Net: hybrid dual-decoder network with SE-convolution transformer for liver tumor segmentationOriginal Research · 2026
5Deep Learning-Based Liver Tumor Segmentation: A Scoping ReviewReview · 2026

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