rishabh sachan | Amity University, Noida (original) (raw)

Papers by rishabh sachan

Research paper thumbnail of Paddy Leaf Disease Detection using Thermal Images and Convolutional Neural Networks

2022 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), May 20, 2022

Research paper thumbnail of An Efficient Algorithm for Object Detection in Thermal Images using Convolutional Neural Networks and Thermal Signature of the Objects

2022 4th International Conference on Energy, Power and Environment (ICEPE), Apr 29, 2022

Research paper thumbnail of Deep Convolutional Neural Network based Detection System for Real-time Corn Plant Disease Recognition

Procedia Computer Science, 2020

Objective To clinically validate a fully automated deep convolutional neural network (DCNN) for d... more Objective To clinically validate a fully automated deep convolutional neural network (DCNN) for detection of surgically proven meniscus tears. Materials and methods One hundred consecutive patients were retrospectively included, who underwent knee MRI and knee arthroscopy in our institution. All MRI were evaluated for medial and lateral meniscus tears by two musculoskeletal radiologists independently and by DCNN. Included patients were not part of the training set of the DCNN. Surgical reports served as the standard of reference. Statistics included sensitivity, specificity, accuracy, ROC curve analysis, and kappa statistics. Results Fifty-seven percent (57/100) of patients had a tear of the medial and 24% (24/100) of the lateral meniscus, including 12% (12/100) with a tear of both menisci. For medial meniscus tear detection, sensitivity, specificity, and accuracy were for reader 1: 93%, 91%, and 92%, for reader 2: 96%, 86%, and 92%, and for the DCNN: 84%, 88%, and 86%. For lateral meniscus tear detection, sensitivity, specificity, and accuracy were for reader 1: 71%, 95%, and 89%, for reader 2: 67%, 99%, and 91%, and for the DCNN: 58%, 92%, and 84%. Sensitivity for medial meniscus tears was significantly different between reader 2 and the DCNN (p = 0.039), and no significant differences existed for all other comparisons (all p ≥ 0.092). The AUC-ROC of the DCNN was 0.882, 0.781, and 0.961 for detection of medial, lateral, and overall meniscus tear. Inter-reader agreement was very good for the medial (kappa = 0.876) and good for the lateral meniscus (kappa = 0.741). Conclusion DCNN-based meniscus tear detection can be performed in a fully automated manner with a similar specificity but a lower sensitivity in comparison with musculoskeletal radiologists.

Research paper thumbnail of Paddy Leaf Disease Detection using Thermal Images and Convolutional Neural Networks

2022 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES)

Research paper thumbnail of An Efficient Algorithm for Object Detection in Thermal Images using Convolutional Neural Networks and Thermal Signature of the Objects

2022 4th International Conference on Energy, Power and Environment (ICEPE)

Research paper thumbnail of Smart Irrigation and Security System for Agricultural Crops and Trees

2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2021

Research paper thumbnail of Paddy Leaf Disease Detection using Thermal Images and Convolutional Neural Networks

2022 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), May 20, 2022

Research paper thumbnail of An Efficient Algorithm for Object Detection in Thermal Images using Convolutional Neural Networks and Thermal Signature of the Objects

2022 4th International Conference on Energy, Power and Environment (ICEPE), Apr 29, 2022

Research paper thumbnail of Deep Convolutional Neural Network based Detection System for Real-time Corn Plant Disease Recognition

Procedia Computer Science, 2020

Objective To clinically validate a fully automated deep convolutional neural network (DCNN) for d... more Objective To clinically validate a fully automated deep convolutional neural network (DCNN) for detection of surgically proven meniscus tears. Materials and methods One hundred consecutive patients were retrospectively included, who underwent knee MRI and knee arthroscopy in our institution. All MRI were evaluated for medial and lateral meniscus tears by two musculoskeletal radiologists independently and by DCNN. Included patients were not part of the training set of the DCNN. Surgical reports served as the standard of reference. Statistics included sensitivity, specificity, accuracy, ROC curve analysis, and kappa statistics. Results Fifty-seven percent (57/100) of patients had a tear of the medial and 24% (24/100) of the lateral meniscus, including 12% (12/100) with a tear of both menisci. For medial meniscus tear detection, sensitivity, specificity, and accuracy were for reader 1: 93%, 91%, and 92%, for reader 2: 96%, 86%, and 92%, and for the DCNN: 84%, 88%, and 86%. For lateral meniscus tear detection, sensitivity, specificity, and accuracy were for reader 1: 71%, 95%, and 89%, for reader 2: 67%, 99%, and 91%, and for the DCNN: 58%, 92%, and 84%. Sensitivity for medial meniscus tears was significantly different between reader 2 and the DCNN (p = 0.039), and no significant differences existed for all other comparisons (all p ≥ 0.092). The AUC-ROC of the DCNN was 0.882, 0.781, and 0.961 for detection of medial, lateral, and overall meniscus tear. Inter-reader agreement was very good for the medial (kappa = 0.876) and good for the lateral meniscus (kappa = 0.741). Conclusion DCNN-based meniscus tear detection can be performed in a fully automated manner with a similar specificity but a lower sensitivity in comparison with musculoskeletal radiologists.

Research paper thumbnail of Paddy Leaf Disease Detection using Thermal Images and Convolutional Neural Networks

2022 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES)

Research paper thumbnail of An Efficient Algorithm for Object Detection in Thermal Images using Convolutional Neural Networks and Thermal Signature of the Objects

2022 4th International Conference on Energy, Power and Environment (ICEPE)

Research paper thumbnail of Smart Irrigation and Security System for Agricultural Crops and Trees

2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2021