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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) EMITTER International Journal of Engineering Technology
Srinivasan, Kavitha
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Computer Science & IT Engineering

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Federated Learning Framework for IID and Non-IID datasets of Medical Images Srinivasan, Kavitha; Prasanna, Sainath; Midha, Rohit; Mohan, Shraddhaa
EMITTER International Journal of Engineering Technology Vol 11 No 1 (2023)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v11i1.742

Abstract

Advances have been made in the field of Machine Learning showing that it is an effective tool that can be used for solving real world problems. This success is hugely attributed to the availability of accessible data which is not the case for many fields such as healthcare, a primary reason being the issue of privacy. Federated Learning (FL) is a technique that can be used to overcome the limitation of availability of data at a central location and allows for training machine learning models on private data or data that cannot be directly accessed. It allows the use of data to be decoupled from the governance (or control) over data. In this paper, we present an easy-to-use framework that provides a complete pipeline to let researchers and end users train any model on image data from various sources in a federated manner. We also show a comparison in results between models trained in a federated fashion and models trained in a centralized fashion for Independent and Identically Distributed (IID) and non IID datasets. The Intracranial Brain Hemorrhage dataset and the Pneumonia Detection dataset provided by the Radiological Society of North America (RSNA) are used for validating the FL framework and comparative analysis.
Semantic based medical visual question answering with explainable artificial intelligence Noor Mohamed, Sheerin Sitara; Srinivasan, Kavitha; Gopalsamy, Raghuraman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2169-2177

Abstract

The medical visual question answering (MVQA) system takes the advantage of both computer vision (CV) and natural language processing (NLP) to accept the medical image and corresponding question as input and generates the respective answer as output. One step further, the MVQA system capable of generating the answer based on the semantics has a distinct place and hence semantic based medical visual question answering (SMVQA) system is proposed in this research. In SMVQA, the semantics for input image and question are generated using layerwise relevance propagation explainable artificial intelligence (LRP XAI) technique and the answer is derived using deductive reasoning method. For this, seven MVQA datasets are used for model creation, testing and validation. The training phase of the SMVQA system is implemented using VGGNet, long short-term memory (LSTM), LRP XAI, ResNet and bidirectional encoder representations from transformers (BERT) to generate a model file. Then the inference is derived in the testing phase based on the generated model file for the test set. Finally, the answer is derived from the inference using natural language toolkit (NLTK) library, term frequency-inverse document frequency (TF-IDF), cosine similarity, best match25 (BM25) techniques along with deductive reasoning. As a result, the proposed SMVQA system gives improved performance then the existing MVQA system especially for abnormality type samples.
Co-Authors Gopalsamy, Raghuraman Midha, Rohit Mohan, Shraddhaa Noor Mohamed, Sheerin Sitara Prasanna, Sainath