Transfer Learning vs. Training from Scratch for Breast Cancer Histology Image Classification

Author(s)	Janhvi Chauhan, Dhaval Modi
Country	India
Abstract	We evaluated the effectiveness of transfer learning compared to fully trained networks for histopathological image classification, using three pre-trained models: VGG16, VGG19, and ResNet50. Their performance was analyzed in the context of magnification-independent breast cancer classification. Additionally, we assessed how varying the training–testing data split impacts model performance. Among the tested configurations, the fine-tuned VGG16 model combined with a logistic regression classifier achieved the highest accuracy of 93.50%, an AUC of 96.00%, and an average precision score (APS) of 96.05% using 85%–15% train–test split. Future work may explore layer-wise fine-tuning and alternative weight initialization strategies to further enhance performance.
Keywords	Breast cancer; Histopathological images; Convolutional neural network; Full training; Transfer learning
Field	Engineering
Published In	Volume 13, Issue 2, July-December 2022
Published On	2022-12-08
Cite This	Transfer Learning vs. Training from Scratch for Breast Cancer Histology Image Classification - Janhvi Chauhan, Dhaval Modi - IJAIDR Volume 13, Issue 2, July-December 2022.

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Journal of Advances in Developmental Research