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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Narasimhamurthy, Shobha
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Automated diagnosis of brain tumor classification and segmentation of magnetic resonance imaging images B. Muddaraju, Chandrakala; Shrinivasa, Shrinivasa; Narasimhamurthy, Shobha; Sontakke, Vaishali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4833-4842

Abstract

Brain tumors are one of the most prevalent disorders of the central nervous system and are dangerous. For patients to receive the best treatment, early diagnosis is crucial. For radiologists to correctly detect brain tumor images, an automated approach is required. The identification procedure can be time-consuming and prone to mistakes. In this work, the issue of fully automated brain tumor classification and segmentation of magnetic resonance imaging (MRI) including meningioma, glioma, pituitary, and no tumor is taken into consideration. In this study, convolutional neural network (CNN) and mask region-based convolutional neural network (R-CNN) are proposed for classification and segmentation problems respectively. This study employed 3,200 images as a training set and the system achieved an accuracy of 96% for classifying the tumors and 94% accuracy in segmentation of tumors.
Shearlet-based texture analysis and deep learning for osteoporosis classification in lumbar vertebrae Ramakrishna, Poorvitha Hullukere; Muddaraju, Chandrakala Beturpalya; Jayaramu, Bhanushree Kothathi; Narasimhamurthy, Shobha
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4318-4331

Abstract

Osteoporosis is a bone disorder characterized by reduced bone density and increased fracture risk. It challenges society's health, remarkably among the elderly population. This research proposed an innovative method by combining Shearlet-transform (ST) spectral analysis with a deep learning neural network (DLNN) and a convolutional neural network (CNN), for osteoporosis classification in lumbar vertebrae (LV) L1-L4 of spine X-ray images. The ST enables precise extraction of texture features from images by capturing significant information regarding trabecular bone micro-architecture and bone mineral density (BMD) variations revealing in osteoporosis regions. These extracted features serve as input to a DLNN for automated classification of osteoporotic and non-osteoporotic vertebrae. Similarly, without extracting any features from ST image is directly used as an input to the CNN to classify the images. The experimental results highlight the framework's effectiveness, achieving 96% accuracy in osteoporosis image classification using CNN. Early and precise detection of osteoporosis, particularly in the lumbar vertebrae, is vital for effective treatment and fracture prevention. This study particularly emphasizes the potential and effectiveness of integrating image spectral analysis technique with NN, to improving diagnostic accuracy and clinical decision-making in osteoporosis management.
Co-Authors B. Muddaraju, Chandrakala Jayaramu, Bhanushree Kothathi Muddaraju, Chandrakala Beturpalya Ramakrishna, Poorvitha Hullukere Shrinivasa, Shrinivasa Sontakke, Vaishali