A Distributed Vision Transformer-Based Deep Learning Framework for Early Detection and Classification of Lumbar Disc Degeneration.
Authors
Affiliations (2)
Affiliations (2)
- Department of Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation; [email protected].
- Department of Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation.
Abstract
Lumbar disc degeneration (LDD) is a principal structural cause of chronic low back pain. Magnetic resonance imaging (MRI) is the reference modality for assessing the intervertebral discs, but severity grading is performed manually, is limited by inter-observer variability, and becomes a bottleneck as imaging volume grows. Convolutional neural networks (CNNs) capture local texture and boundary detail efficiently, yet their restricted receptive field limits the modeling of relationships extending across several vertebral levels, whereas Vision Transformers (ViTs) represent such global context through self-attention. This protocol describes a distributed CNN+ViT framework in which a ResNet-50 backbone extracts hierarchical spatial features, a ViT encoder models global context across the extracted feature map, and the Hadoop Distributed File System (HDFS) with Apache Spark provides distributed storage and data-parallel training. The dataset consisted of 5,000 expert-labeled sagittal T2-weighted lumbar spine MRI slices from 625 patients, annotated with the Pfirrmann grading system and partitioned at the patient level into stratified training, validation, and test subsets so that no patient contributed images to more than one subset. Each slice was resized to 224 pixels x 224 pixels, intensity-normalized, and augmented during training only. Class imbalance was addressed with a weighted cross-entropy objective. On a held-out test subset of 100 slices the framework achieved 95.9% precision, 94.0% recall, 94.9% F1-score, and 95.0% accuracy for the binary discrimination of degenerated from normal discs, against F1-scores of 88.9% for AlexNet and 90.9% for VGG-16 trained under an identical protocol, and the ordering of the three models was preserved on test subsets of 200 and 300 slices. Four-node distributed training reduced wall-clock time by 1.60x to 2.26x relative to a single node without changing the classification procedure. The framework is presented as a reproducible research pipeline; independent external validation is required before any clinical use.