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Go to Editorial ManagerAlzheimer’s disease (AD), the most common form of dementia, affects over 55 million people worldwide. The most form of dementia progresses into three distinct stages: mild, moderate, and very mild compared to Cognitively Normal (CN). Early detection is crucial to prevent brain damage before the late stages. Convolutional Neural Networks (CNNs), a subfield of deep learning, have recently found remarkable applications in medical image processing and computer-aided diagnosis (CAD). To this end, this paper presents a new efficient multi-classification AlzCNN-Net model to enhance the accuracy and efficacy of MRI image classification for various Alzheimer’s disease conditions. Initially, the training process involves utilizing open-source Alzheimer’s disease datasets from the Kaggle database to classify the brain MRI into its corresponding category. To verify the model’s efficacy, a comparative analysis with three pre-trained models, namely VGG16, Incep-tionV3, and MobileNetV2, has been investigated via transfer learning applied to the same dataset. As a result, the findings reveal that the AlzCNN-Net model exhibits an optimal performance, attaining the best accuracy in training with 99.67%, validation with 98.24%, and testing with 98.9% accuracy at epoch 100 with batch size 32 compared to the existing pre-trained approaches.
Lumbar spine stenosis (LSS) is a common reason for low back pain, which refers to anatomical spinal canal stenosis. It often causes pressure on the nerve elements due to the surrounding soft tissue and bone. Due to the huge number of spinal injuries, manual diagnosis of lumbar spine stenosis by radiologists is tedious or time-consuming. Therefore, Deep learning techniques have become a more helpful tool to overcome this problem. For this purpose, this study employed the YOLO-v5 to develop an LSS detection model on a dataset of lumbar spine MRI scans from 153 patients with symptomatic low back pain. The dataset was filtered to include 84 mid-sagittal images using annotation techniques. The detection model is utilized to classify the intervertebral disc (IVD) condition as either bulging or normal. The results obtained showed that the model achieved an accuracy exceeding 88% in detecting and classifying the lumbar spine vertebra. The developed models showed that they are effective for lumbar intervertebral disc classification.