Revolutionizing Breast Cancer Diagnosis: A Concatenated Precision through Transfer Learning in Histopathological Data...

Area of Science:

• Oncology
• Medical Imaging
• Artificial Intelligence

Background:

• Breast cancer diagnosis relies heavily on accurate histopathological analysis.
• Deep learning shows promise in enhancing histopathological data analysis efficiency and precision.

Purpose of the Study:

• To develop and evaluate a novel Transfer Learning-based concatenated model for breast cancer histopathology.
• To improve the accuracy and efficiency of breast cancer diagnosis through advanced AI techniques.

Main Methods:

• Utilized Transfer Learning by adapting pre-trained Convolutional Neural Network (CNN) models (VGG-16, MobileNetV2, ResNet50, DenseNet121).
• Developed a four-level concatenated classification model, optimizing hyperparameters for performance.
• Benchmarked the concatenated model against individual classifiers on histopathological data.

Main Results:

• The Transfer Learning-based concatenated model achieved a training accuracy of 98%.
• Demonstrated substantial performance enhancements compared to traditional methodologies and individual classifiers.
• The four-level concatenated model significantly advanced the accuracy of breast cancer histopathological data analysis.

Conclusions:

• The proposed concatenated model effectively leverages deep learning and Transfer Learning for breast cancer histopathology.
• This approach has the potential to augment pathologists' diagnostic capabilities, leading to better treatment planning.
• Represents a significant advancement in AI for improved breast cancer understanding and management.