Ovarian cancer is a histologically heterogeneous malignancy in which accurate subtype classification is essential for progno
sis and treatment selection. Manual interpretation of hematoxylin and eosin (H&E)–stained histopathology slides remain
time-consuming and subject to inter-observer variability, particularly for morphologically overlapping subtypes. In this
study, an interpretable deep learning framework was developed for automated ovarian cancer subtype classification using a
fine-tuned ResNet50 architecture. A publicly available histopathology dataset comprising five major ovarian carcinoma sub
types was employed. Model training incorporated optimized strategies including data augmentation, selective layer unfreez
ing, label smoothing, and test-time augmentation. Classification performance was benchmarked against established convolu
tional neural network architectures. Visual interpretability was assessed using Gradient-weighted Class Activation Mapping
(Grad-CAM) to examine model attention patterns.
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