What question did this study set out to answer?

The aim is to create a framework for identifying and classifying brain and breast tumors using deep learning techniques.

Deep learning based identification and analysis of brain and breast tumours using VGG16 ResNet50 and EfficientNetB0

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The paper demonstrates a novel multi-modal approach to classify brain and breast tumors, suggesting improved diagnostic accuracy in medical imaging.

The aim is to create a framework for identifying and classifying brain and breast tumors using deep learning techniques.
Developed a multi-modal approach using VGG-16, ResNet-50, and EfficientNet-B0 architectures.
Conducted experiments on MIAS and Br35H datasets for classification tasks.
Employed techniques like data augmentation to address class imbalances.
Implemented early stopping to mitigate overfitting.
Standardized hyperparameter settings across all deep learning models.
Achieved 98.61% accuracy on the MIAS dataset and 98.33% accuracy on the Br35H dataset.
EfficientNet-B0 outperformed other architectures when using Stochastic Gradient Descent as the optimizer.

Imran et al. (2026) studied this question.

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The paper demonstrates a novel multi-modal approach to classify brain and breast tumors, suggesting improved diagnostic accuracy in medical imaging.