A deep learning framework for glaucoma detection based on robust optic disc segmentation and transfer learning

2021 ◽  
Author(s):  
Deepa Natarajan ◽  
Esakkirajan Sankaralingam ◽  
Keerthiveena Balraj ◽  
Selvakumar Karuppusamy
Keyword(s):  
Deep Learning ◽  
Optic Disc ◽  
Transfer Learning ◽  
Learning Framework ◽  
Glaucoma Detection

Glaucoma Detection by Artificial Intelligence: GlauNet A Deep Learning Framework

2021 ◽  
Author(s):  
Alexandru Lavric ◽  
Adrian I. Petrariu ◽  
Stefan Havriliuc ◽  
Eugen Coca
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Learning Framework ◽  
Glaucoma Detection

scholarly journals A coarse-to-fine deep learning framework for optic disc segmentation in fundus images

2019 ◽  
Vol 51 ◽  
pp. 82-89 ◽  
Author(s):  
Lei Wang ◽  
Han Liu ◽  
Yaling Lu ◽  
Hang Chen ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optic Disc ◽  
Fundus Images ◽  
Learning Framework ◽  
Coarse To Fine

scholarly journals A Transfer Learning–Based Active Learning Framework for Brain Tumor Classification

2021 ◽  
Vol 4 ◽  
Author(s):  
Ruqian Hao ◽  
Khashayar Namdar ◽  
Lin Liu ◽  
Farzad Khalvati
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Active Learning ◽  
Transfer Learning ◽  
Medical Images ◽  
Tumor Classification ◽  
Training Data ◽  
Validation Dataset ◽  
Low Grade ◽  
Learning Framework

Brain tumor is one of the leading causes of cancer-related death globally among children and adults. Precise classification of brain tumor grade (low-grade and high-grade glioma) at an early stage plays a key role in successful prognosis and treatment planning. With recent advances in deep learning, artificial intelligence–enabled brain tumor grading systems can assist radiologists in the interpretation of medical images within seconds. The performance of deep learning techniques is, however, highly depended on the size of the annotated dataset. It is extremely challenging to label a large quantity of medical images, given the complexity and volume of medical data. In this work, we propose a novel transfer learning–based active learning framework to reduce the annotation cost while maintaining stability and robustness of the model performance for brain tumor classification. In this retrospective research, we employed a 2D slice–based approach to train and fine-tune our model on the magnetic resonance imaging (MRI) training dataset of 203 patients and a validation dataset of 66 patients which was used as the baseline. With our proposed method, the model achieved area under receiver operating characteristic (ROC) curve (AUC) of 82.89% on a separate test dataset of 66 patients, which was 2.92% higher than the baseline AUC while saving at least 40% of labeling cost. In order to further examine the robustness of our method, we created a balanced dataset, which underwent the same procedure. The model achieved AUC of 82% compared with AUC of 78.48% for the baseline, which reassures the robustness and stability of our proposed transfer learning augmented with active learning framework while significantly reducing the size of training data.

A deep learning framework for Hybrid Heterogeneous Transfer Learning

2019 ◽  
Vol 275 ◽  
pp. 310-328 ◽  
Author(s):  
Joey Tianyi Zhou ◽  
Sinno Jialin Pan ◽  
Ivor W. Tsang
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Learning Framework

A Deep Learning Framework for Automated Transfer Learning of Neural Networks

2019 ◽  
Author(s):  
Thanasekhar Balaiah ◽  
Timothy Jones Thomas Jeyadoss ◽  
Sri Sainee Thirumurugan ◽  
Rahul Chander Ravi
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Transfer Learning ◽  
Learning Framework

DeepHBSP: A Deep Learning Framework for Predicting Human Blood-Secretory Proteins Using Transfer Learning

2021 ◽  
Vol 36 (2) ◽  
pp. 234-247
Author(s):  
Wei Du ◽  
Yu Sun ◽  
Hui-Min Bao ◽  
Liang Chen ◽  
Ying Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Human Blood ◽  
Secretory Proteins ◽  
Learning Framework

scholarly journals A Deep Learning-Based Transfer Learning Framework for the Early Detection and Classification of Dermoscopic Images of Melanoma

2021 ◽  
Vol 14 (3) ◽  
pp. 1231-1247
Author(s):  
Lokesh Singh ◽  
Rekh Ram Janghel ◽  
Satya Prakash Sahu
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Transfer Learning ◽  
Skin Lesions ◽  
Learning Framework ◽  
Melanoma Detection ◽  
Pigmented Skin Lesions ◽  
Sensitivity Specificity ◽  
Aided Diagnosis

Purpose:Less contrast between lesions and skin, blurriness, darkened lesion images, presence of bubbles, hairs are the artifactsmakes the issue challenging in timely and accurate diagnosis of melanoma. In addition, huge similarity amid nevus lesions and melanoma pose complexity in investigating the melanoma even for the expert dermatologists. Method: In this work, a computer-aided diagnosis for melanoma detection (CAD-MD) system is designed and evaluated for the early and accurate detection of melanoma using thepotentials of machine, and deep learning-based transfer learning for the classification of pigmented skin lesions. The designed CAD-MD comprises of preprocessing, segmentation, feature extraction and classification. Experiments are conducted on dermoscopic images of PH2 and ISIC 2016 publicly available datasets using machine learning and deep learning-based transfer leaning models in twofold: first, with actual images, second, with augmented images. Results:Optimal results are obtained on augmented lesion images using machine learning and deep learning models on PH2 and ISIC-16 dataset. The performance of the CAD-MD system is evaluated using accuracy, sensitivity, specificity, dice coefficient, and jacquard Index. Conclusion:Empirical results show that using the potentials of deep learning-based transfer learning model VGG-16 has significantly outperformed all employed models with an accuracy of 99.1% on the PH2 dataset.

scholarly journals Segmenting the Optic Disc Using a Deep Learning Ensemble Model Based on OWA Operators

2021 ◽  
Author(s):  
Mohammed Yousef Salem Ali ◽  
Mohamed Abdel-Nasser ◽  
Mohammed Jabreel ◽  
Aida Valls ◽  
Marc Baget
Keyword(s):  
Deep Learning ◽  
Optic Disc ◽  
Vision Loss ◽  
Semantic Segmentation ◽  
Medical Image Segmentation ◽  
Weighted Averaging ◽  
Fundus Images ◽  
Owa Operators ◽  
Detection Systems ◽  
Glaucoma Detection

The optic disc (OD) is the point where the retinal vessels begin. OD carries essential information linked to Diabetic Retinopathy and glaucoma that may cause vision loss. Therefore, accurate segmentation of the optic disc from eye fundus images is essential to develop efficient automated DR and glaucoma detection systems. This paper presents a deep learning-based system for OD segmentation based on an ensemble of efficient semantic segmentation models for medical image segmentation. The aggregation of the different DL models was performed with the ordered weighted averaging (OWA) operators. We proposed the use of a dynamically generated set of weights that can give a different contribution to the models according to their performance during the segmentation of OD in the eye fundus images. The effectiveness of the proposed system was assessed on a fundus image dataset collected from the Hospital Sant Joan de Reus. We obtained Jaccard, Dice, Precision, and Recall scores of 95.40, 95.10, 96.70, and 93.90%, respectively.

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