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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics
Boruah, Kuntala
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Early Detection Of Canine Babesia From Red Blood Cell Images Using Deep Ensemble Learning Baruah, Dilip Kumar; Boruah, Kuntala
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.484

Abstract

Artificial intelligence-assisted medical diagnosis is enhancing accuracy with the contribution of several state of the art technologies such as Deep Learning (DL), Machine Learning (ML) and Image Processing (IP). From the detection of diseases to the selection of proper treatment plans, AI-powered assistance is effectively employed by healthcare professionals. Despite these advancements, the application of AI in animal healthcare is lagging behind, presenting a significant scope for AI adoption in veterinary medical diagnostics. This study addresses this gap by focusing on the automated diagnosis of canine Babesia infection, a parasitic disease that affects red blood cells (RBC). Our research contributed by developing a labeled dataset of microscopic images of red blood cells of infected and uninfected cases. During this work, four AI models are developed for automated classification: a custom Convolutional Neural Network (CNN), two pre-trained models (VGG16 ,DenseNet121) and a hybrid model (DenseNet121 + Support Vector Machine (SVM)). The performance of these models was 96.88%, 94%, 96.37% and 95.50% respectively. To further enhance the accuracy, a weighted average ensemble technique was employed. The ensemble model achieved an improved accuracy of 97.75%, demonstrating its potential. The enhanced performance of the ensemble model highlights the effectiveness of our method, significantly outperforming traditional methods and providing veterinarians with an efficient early diagnosis tool. This study is one of the few to address disease detection from microscopic images in animals using the potential of Artificial Intelligence.
Predicting Evolutionary Importance of Amino Acids through Mutation of Codons Using K-means Clustering Hussain, Nasrin Irshad; Boruah, Kuntala; Akhtar, Adil
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.538

Abstract

Mutation is a random biological event that may cause permanent (long term) change in living organism induced by several structural or composition alteration in the proteins. During mutation genetic materials such as nucleotide bases in the codons is changed which potentially contributed to the alteration in the codons and consequently the amino acid that new codon encodes. In this study mutation at different nucleotide base positions within the codons is analyzed to understand the evolutionary importance of amino acids. By creating hypothetical mutations at the first, second and third positions of all 61 codons (excluding stop codons) and using K-means clustering, we categorized the resulting amino acids. Our analysis reveals that mutations at the second base position generate the highest number of distinct amino acids, indicating greater evolutionary significance compared to first and third position mutations. We applied the proposed framework on COVID-2 SARS-CoV-2 (Homo sapiens) amino acid sequence and are able to deduce several significant findings about the mutation patterns. The clustering analysis revealed that amino acids such as Glycine (G), Alanine (A), Proline (P), Valine (V) and one polar amino acid are recurrent in the combined centroids of the clusters. These amino acids, predominantly hydrophobic, play a crucial role in stabilizing protein structures. This framework not only gives the insight understanding of mutation patterns and their biological significance but also underscores the importance of specific amino acids in the evolutionary process.
Co-Authors Akhtar, Adil Baruah, Dilip Kumar Hussain, Nasrin Irshad