an-experimental-protocol-for-explainable-ai-driven-secure-cloud-data

An Experimental Protocol for Explainable AI-Driven Secure Cloud Data Migration Using Synthetic Healthcare Data

In healthcare systems, more and more cloud data migration is being done, but this also changes the times when data transfer is probably the biggest risk ...

fedmediformer-xai-federated-multimodal-transformers-with-diffusion

FedMediFormer-XAI: Federated Multimodal Transformers with Diffusion Augmentation and Graph-Based Drug Recommendation for Diabetes

Diabetes management faces a number of obstacles, such as fragmented healthcare data, privacy concerns, poor explainability, and the lack of personalized ...

experimental-protocols-for-developing-validating-explainable-ai

Experimental Protocols for Developing and Validating Explainable AI Models in Secure Healthcare Diagnostics

Artificial Intelligence (AI) has drastically enhanced healthcare diagnostics with automated disease detection, predictive analytics, and intelligent ...

kathmandu university

The research interest in this field is that females are not aware of their health conditions until they develop tumour, especially when breast cancer is concerned. The breast cancer risk factors include genetics, heredity, and sedentary lifestyle. The prime concern for the mortality rate among females is breast cancer, and breast cancer is on the rise, both in rural and urban India. Women aged 45 or above are more vulnerable to this disease. Images are more effective at depicting information as compared to text. With the advancement in technology, several computerized techniques have come up to extract hidden information from the images. The processed images have found their application in several sectors and medical science is one of them. Disease-like breast cancer affects most women universally and it happens due to the existence of breast masses in the breast region for the development of breast cancer in women. Timely breast cancer detection can also increase the rate of effective treatment and the survival of women suffering from breast cancer. This work elaborates the method of performing hybrid segmentation techniques using CLAHE, morphological operations on mammogram images, and classified images using deep learning. Images from the MIAS database have been used to obtain readings for parameters: threshold, accuracy, sensitivity, specificity rate, biopsy rate, or a combination of all the parameters and many others under study.

ArticleTotal : 3	Year
Publication title	2026
Publication title	2026
Publication title	2026

Sudan Jha

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