Annex Publishers

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.

Media strongly influences children’s eating habits, promoting consumption of sugary and ultra-processed foods.

Fusarium wilt disease of tomato is caused by Fusarium oxysporum f.sp. lycopersici and is a limiting factor to tomato production in Nigeria.

Sub-Saharan Africa has experienced substantial declines in new HIV infections over the past decade; however, these gains have not been uniform across populations. In Mozambique, married women remain a largely overlooked group in HIV prevention strategies, despite persistently high HIV prevalence.

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.

Media strongly influences children’s eating habits, promoting consumption of sugary and ultra-processed foods.

Fusarium wilt disease of tomato is caused by Fusarium oxysporum f.sp. lycopersici and is a limiting factor to tomato production in Nigeria.

Sub-Saharan Africa has experienced substantial declines in new HIV infections over the past decade; however, these gains have not been uniform across populations. In Mozambique, married women remain a largely overlooked group in HIV prevention strategies, despite persistently high HIV prevalence.