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All Journal International Journal of Advances in Applied Sciences Journal of Information Systems Engineering and Business Intelligence
Ramaraj, Vijayan
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Computer Science & IT Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Medical Image Fusion for Brain Tumor Diagnosis Using Effective Discrete Wavelet Transform Methods Ramaraj, Vijayan; Venkatachalaappaswamy, Mareeswari; Sankar , Manoj Kumar
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.70-80

Abstract

Background: The field of clinical or medical imaging is beginning to experience significant advancements in recent years. Various medical imaging methods such as computed tomography (CT), X-radiation (X-ray), and magnetic resonance imaging (MRI) produce images with distinct resolution differences, goals, and noise levels, making it challenging for medical experts to diagnose diseases. Objective: The limitations of a single medical image modality have increased the necessity for medical image fusion. The proposed solution is to create a fusion method of merging two types of medical images, such as MRI and CT. Therefore, this study aimed to develop a software solution that swiftly identifies the precise region of a brain tumor, speeding up the diagnosis and treatment planning. Methods: The proposed methodology combined clinical images by using discrete wavelet transform (DWT) and inverse discrete wavelet transform (IDWT). This strategy depended on a multi-goal decay of the image information using DWT, and high-frequency sub-bands of the disintegrated images were combined using a weighted averaging method. Meanwhile, the low-frequency sub-bands were straight-forwardly replicated in the resulting image. The combined high-quality image was recreated using the IDWT. This method can handle images with various modalities and resolutions without the need for previous data. Results: The results showed that the outcomes of the proposed method were assessed by different metrics such as accuracy, recall, F1-score, and visual quality. The method showed a high accuracy of 98% over the familiar neural network techniques. Conclusion: The proposed method was found to be computationally effective and produced high-quality medical images to assist professionals. Furthermore, the method can be stretched out to other image modalities and exercised by hybrid techniques of wavelet transform and neural networks and used for different clinical image analysis tasks.   Keywords: CT and MRI, Image fusion, brain tumor, wavelet transform methods, medical images, machine learning, CNN  
Improving the BERT model for long text sequences in question answering domain Ramaraj, Vijayan; Appa Swamy, Mareeswari Venkatachala; Prince, Ephzibah Evan; Kumar, Chandhan
International Journal of Advances in Applied Sciences Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i1.pp106-115

Abstract

The text-based question-answering (QA) system aims to answer natural language questions by querying the external knowledge base. It can be applied to real-world systems like medical documents, research papers, and crime-related documents. Using this system, users don't have to go through the documents manually the system will understand the knowledge base and find the answer based on the text and question given to the system. Earlier state-of-the-art natural language processing (NLP) was recurrent neural network (RNN) and long short-term memory (LSTM). As a result, these models are hard to parallelize and poor at retaining contextual relationships across long text inputs. Today, bidirectional encoder representations from transformers (BERT) are the contemporary algorithm for NLP. BERT is not capable of handling long text sequences; it can handle 512 tokens at a time which makes it difficult for long context. Smooth inverse frequency (SIF) and the BERT model will be incorporated together to solve this challenge. BERT trained on the Stanford question answering dataset (SQuAD) and SIF model demonstrates robustness and effectiveness on long text sequences from different domains. Experimental results suggest that the proposed approach is a promising solution for QA on long text sequences.
Co-Authors Appa Swamy, Mareeswari Venkatachala Kumar, Chandhan Prince, Ephzibah Evan Sankar , Manoj Kumar Venkatachalaappaswamy, Mareeswari