scholarly journals Automated Detection and Classification of Oral Lesions Using Deep Learning for Early Detection of Oral Cancer

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 132677-132693 ◽  
Author(s):  
Roshan Alex Welikala ◽  
Paolo Remagnino ◽  
Jian Han Lim ◽  
Chee Seng Chan ◽  
Senthilmani Rajendran ◽  
...  
Keyword(s):  
Deep Learning ◽  
Oral Cancer ◽  
Early Detection ◽  
Automated Detection ◽  
Oral Lesions

Automated detection and classification of shoulder arthroplasty models using deep learning

2020 ◽  
Vol 49 (10) ◽  
pp. 1623-1632
Author(s):  
Paul H. Yi ◽  
Tae Kyung Kim ◽  
Jinchi Wei ◽  
Xinning Li ◽  
Gregory D. Hager ◽  
...  
Keyword(s):  
Deep Learning ◽  
Shoulder Arthroplasty ◽  
Automated Detection

scholarly journals Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Plants ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1302 ◽  
Author(s):  
Reem Ibrahim Hasan ◽  
Suhaila Mohd Yusuf ◽  
Laith Alzubaidi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Early Detection ◽  
Feature Fusion ◽  
State Of The Art ◽  
Vital Role ◽  
Considerable Improvement ◽  
Plant Diseases ◽  
Recent Emergence

Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.

scholarly journals Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model

2020 ◽  
Vol 133 ◽  
pp. 210-216 ◽  
Author(s):  
K. Shankar ◽  
Abdul Rahaman Wahab Sait ◽  
Deepak Gupta ◽  
S.K. Lakshmanaprabu ◽  
Ashish Khanna ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Learning Model ◽  
Automated Detection ◽  
Deep Learning Model

scholarly journals Deep learning pipeline for automated detection and classification of central canal, lateral recess and neural foraminal stenosis on lumbar spine MRI

2021 ◽  
Vol 1 ◽  
pp. 100240
Author(s):  
Jiong Hao Tan ◽  
Lei Zhu ◽  
Kaiyuan Yang ◽  
Hiroshi Yoshioka ◽  
Beng Chin Ooi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lumbar Spine ◽  
Central Canal ◽  
Automated Detection ◽  
Foraminal Stenosis ◽  
Lateral Recess ◽  
Spine Mri

scholarly journals Comparison of Object Detection Approaches Applied to Field Images of Papanicolaou Stained Cytology Slides

2021 ◽  
Author(s):  
André Victória Matias ◽  
Allan Cerentini ◽  
Luiz Antonio Buschetto Macarini ◽  
João Gustavo Atkinson Amorim ◽  
Felipe Perozzo Daltoé ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Deep Learning ◽  
Oral Cancer ◽  
Global Health ◽  
Object Detection ◽  
Learning Approach ◽  
Non Invasive ◽  
Global Health Issue ◽  
Invasive Method

Papanicolaou is an inexpensive and non-invasive method, generally applied to detect cervical cancer, that can also be useful to detect cancer on oral cavities. Although oral cancer is considered a global health issue with 350.000 people diagnosed over a year it can successfully be treated if diagnosed at early stages. The manual process of analyzing cells to detect abnormalities is time-consuming and subject to variations in perceptions from different professionals. To evaluate a possible solution to the automation of this process, in this paper we employ the object detection deep learning approach in the analysis of this type of image using 3 models: RetinaNet, Faster R-CNN, and Mask R-CNN. We trained and tested the models using images from 6 cytology slides (4 cancer cases and 2 healthy samples) and our results show that Mask R-CNN was the best model for localization and classification of nuclei with an IoU of 0.51 and recall of abnormal nuclei of 0.67.

scholarly journals An Automated Detection and Classification of Cataract and Glaucoma Using RCNN

2021 ◽  
Vol 9 (VI) ◽  
pp. 4959-4967
Author(s):  
Alida E T
Keyword(s):  
Neural Network ◽  
Early Detection ◽  
Automated Detection ◽  
Classification Approach ◽  
Glaucoma Screening ◽  
Glaucoma Detection ◽  
Computer Aided Analysis ◽  
The World ◽  
Computer Aided

one of the human’s deterioration is visual impairment. Cataract and Glaucoma are the most prevailing cause blindness in the world. Early detection and treatment is the best way to prevent the blindness. Currently grading is done by human graders, it is found to be time taking and grading is usually subjective. Computer aided analysis can help human graders. An automated cataract and glaucoma detection and classification approach is proposed in this paper, to grade more objectively. Region based convolution neural network (RCNN) is used to classification process. The percentage of accuracy of classification obtained for cataract and glaucoma is 98.9% and 97.8% respectively. The method is especially suitable for cataract and glaucoma screening in the underdeveloped areas or areas which are in shortage of ophthalmic resources. It can also improve the accessibility of ophthalmic medical treatment.

scholarly journals Automated detection and classification of the proximal humerus fracture by using deep learning algorithm

2018 ◽  
Vol 89 (4) ◽  
pp. 468-473 ◽  
Author(s):  
Seok Won Chung ◽  
Seung Seog Han ◽  
Ji Whan Lee ◽  
Kyung-Soo Oh ◽  
Na Ra Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Proximal Humerus Fracture ◽  
Proximal Humerus ◽  
Learning Algorithm ◽  
Automated Detection ◽  
Humerus Fracture ◽  
Deep Learning Algorithm
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