scholarly journals Classification of Sagittal Lumbar Spine MRI for Lumbar Spinal Stenosis Detection Using Transfer Learning of a Deep Convolutional Neural Network

2022 ◽  
pp. 149-160
Author(s):  
Friska Natalia ◽  
Sud Sudirman
Keyword(s):  
Neural Network ◽  
Lumbar Spine ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Spinal Stenosis ◽  
Lumbar Spinal Stenosis ◽  
Stenosis Detection ◽  
Lumbar Spinal ◽  
Spine Mri

The association between spondylolisthesis and decreased muscle health throughout the lumbar spine for patients with operative lumbar spinal stenosis

2021 ◽  
Author(s):  
Sohrab Virk ◽  
Milan Sandhu ◽  
Joshua Wright-Chisem ◽  
Avani Vaishnav ◽  
Todd Albert ◽  
...  
Keyword(s):  
Lumbar Spine ◽  
Spinal Stenosis ◽  
Lumbar Spinal Stenosis ◽  
Muscle Health ◽  
Lumbar Spinal

Long-Term Outcomes of Surgical and Nonsurgical Management of Lumbar Spinal Stenosis: 8 to 10 Year Results from the Maine Lumbar Spine Study

Spine ◽  
2005 ◽  
Vol 30 (8) ◽  
pp. 936-943 ◽  
Author(s):  
Steven J. Atlas ◽  
Robert B. Keller ◽  
Yen A. Wu ◽  
Richard A. Deyo ◽  
Daniel E. Singer
Keyword(s):  
Lumbar Spine ◽  
Spinal Stenosis ◽  
Lumbar Spinal Stenosis ◽  
Long Term Outcomes ◽  
Nonsurgical Management ◽  
Lumbar Spinal

Classification of Batik Authenticity Using Convolutional Neural Network Algorithm with Transfer Learning Method

2021 ◽  
Author(s):  
Farrel Athaillah Putra ◽  
Dwi Anggun Cahyati Jamil ◽  
Briliantino Abhista Prabandanu ◽  
Suhaili Faruq ◽  
Firsta Adi Pradana ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Learning Method ◽  
Network Algorithm ◽  
Neural Network Algorithm

Tomato pest classification using deep convolutional neural network with transfer learning, fine tuning and scratch learning

2021 ◽  
pp. 1-10
Author(s):  
Gayatri Pattnaik ◽  
Vimal K. Shrivastava ◽  
K. Parvathi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Data Augmentation ◽  
State Of The Art ◽  
Deep Convolutional Neural Network ◽  
Fine Tuning ◽  
Tomato Plants ◽  
Random Weights

Pests are major threat to economic growth of a country. Application of pesticide is the easiest way to control the pest infection. However, excessive utilization of pesticide is hazardous to environment. The recent advances in deep learning have paved the way for early detection and improved classification of pest in tomato plants which will benefit the farmers. This paper presents a comprehensive analysis of 11 state-of-the-art deep convolutional neural network (CNN) models with three configurations: transfers learning, fine-tuning and scratch learning. The training in transfer learning and fine tuning initiates from pre-trained weights whereas random weights are used in case of scratch learning. In addition, the concept of data augmentation has been explored to improve the performance. Our dataset consists of 859 tomato pest images from 10 categories. The results demonstrate that the highest classification accuracy of 94.87% has been achieved in the transfer learning approach by DenseNet201 model with data augmentation.

scholarly journals Lumbar spinal stenosis and disc alterations affect the upper lumbar spine in adults with achondroplasia

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Thomas Huet ◽  
Martine Cohen-Solal ◽  
Jean-Denis Laredo ◽  
Corinne Collet ◽  
Geneviève Baujat ◽  
...  
Keyword(s):  
Lumbar Spine ◽  
Spinal Stenosis ◽  
Lumbar Spinal Stenosis ◽  
Lumbar Spinal

scholarly journals Redundant nerve roots in lumbar spinal stenosis: inter- and intra-rater reliability of an MRI-based classification

2019 ◽  
Vol 62 (2) ◽  
pp. 223-230 ◽  
Author(s):  
Luca Papavero ◽  
Carlos J. Marques ◽  
Jens Lohmann ◽  
Thies Fitting ◽  
Kathrin Schawjinski ◽  
...  
Keyword(s):  
Spinal Stenosis ◽  
Lumbar Spinal Stenosis ◽  
Nerve Roots ◽  
Severe Stenosis ◽  
Rater Reliability ◽  
Microsurgical Decompression ◽  
Orthopedic Surgeons ◽  
Symptom History ◽  
Lumbar Spinal

Abstract Purpose Patients with central lumbar spinal stenosis (LSS) have a longer symptom history, more severe stenosis, and worse postoperative outcomes, when redundant nerve roots (RNRs) are evident in the preoperative MRI. The objective was to test the inter- and intra-rater reliability of an MRI-based classification for RNR. Methods This is a retrospective reliability study. A neuroradiologist, an orthopedic surgeon, a neurosurgeon, and three orthopedic surgeons in-training classified RNR on 126 preoperative MRIs of patients with LSS admitted for microsurgical decompression. On sagittal and axial T2-weighted images, the following four categories were classified: allocation (A) of the key stenotic level, shape (S), extension (E), and direction (D) of the RNR. A second read with cases ordered differently was performed 4 weeks later. Fleiss and Cohen’s kappa procedures were used to determine reliability. Results The allocation, shape, extension, and direction (ASED) classification showed moderate to almost perfect inter-rater reliability, with kappa values (95% CI) of 0.86 (0.83, 0.90), 0.62 (0.57, 0.66), 0.56 (0.51, 0.60), and 0.66 (0.63, 0.70) for allocation, shape, extension, and direction, respectively. Intra-rater reliability was almost perfect, with kappa values of 0.90 (0.88, 0.92), 0.86 (0.84, 0.88), and 0.84 (0.81, 0.87) for shape, extension, and direction, respectively. Intra-rater kappa values were similar for junior and senior raters. Kappa values for inter-rater reliability were similar between the first and second reads (p = 0.06) among junior raters and improved among senior raters (p = 0.008). Conclusions The MRI-based classification of RNR showed moderate-to-almost perfect inter-rater and almost perfect intra-rater reliability.

Sign in / Sign up

Export Citation Format

Share Document