Computer-aided diagnosis of cataract using deep transfer learning

Pratap, Turimerla; Kokil, Priyanka

doi:10.1016/j.bspc.2019.04.010

Cited by 100 publications

(62 citation statements)

References 24 publications

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“…41 Turimerla Pratap et al used convolutional neural networks to diagnose cataracts. 42 Tao Li et al used a deep learning model to screen diabetic retinopathy. 43 Rory Sayres et al used a convolutional neural network to grade diabetic retinopathy.…”

Section: Discussionmentioning

confidence: 99%

<p>Identifying Diabetic Macular Edema and Other Retinal Diseases by Optical Coherence Tomography Image and Multiscale Deep Learning</p>

Zhang¹,

Liu²,

Li³

et al. 2020

DMSO

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Purpose: Diabetic Macular Edema has been one of the research hotspots all over the world. But as the global population continues to grow, the number of OCT images requiring manual analysis is becoming increasingly unaffordable. Medical images are often fuzzy due to the inherent physical processes of acquiring them. It is difficult for traditional algorithms to use low-quality data. And traditional algorithms usually only provide diagnostic results, which makes the reliability and interpretability of the model face challenges. To solve problem above, we proposed a more intuitive and robust diagnosis model with self-enhancement ability and clinical triage patients' ability. Methods: We used 38,057 OCT images (Drusen, DME, CNV and Normal) to establish and evaluate the model. All data are OCT images of fundus retina. There were 37,457 samples in the training dataset and 600 samples in the validation dataset. In order to diagnose these images accurately, we propose a multiscale transfer learning algorithm. Firstly, the sample is sent to the automatic self-enhancement module for edge detection and enhancement. Then, the processed data are sent to the image diagnosis module to determine the disease type. This process makes more data more effective and can be accurately classified. Finally, we calculated the accuracy, precision, sensitivity and specificity of the model, and verified the performance of the model from the perspective of clinical application. Results: The model proposed in this paper can provide the diagnosis results and display the detection targets more intuitively. The model reached 94.5% accuracy, 97.2% precision, 97.7% sensitivity and 97% specificity in the independent testing dataset. Conclusion: Comparing the performance of relevant work and ablation test, our model achieved relatively good performance. It is proved that the model proposed in this paper has a stronger ability to recognize diseases even in the face of low-quality images. Experiment results also demonstrate its clinical referral capability. It can reduce the workload of medical staff and save the precious time of patients.

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Section: Discussionmentioning

confidence: 99%

<p>Identifying Diabetic Macular Edema and Other Retinal Diseases by Optical Coherence Tomography Image and Multiscale Deep Learning</p>

Zhang¹,

Liu²,

Li³

et al. 2020

DMSO

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“…Dataset Zhang et al [9] Ran et al [75] Shaharum et al [95] Abbas et al [71], Yu et al [58], Diaz et al [39], De et al [42], Gomez et al [44] Yang et al [76], Pratap et al [50], Doshi et al [51] Sahlsten et al [49] Sisodia et al [78] Antal et al [79] Orlando et al [72] Almazroa et al [82] Chen et al [62], Ceretinia et al [40], Perdomo et al [26] Orlando et al [41] Phan et al [35] Dong et al [70] Asaoka et al [37], An et al [38], Nguyen et al [31], Xu et al [56] Pal et al [64] Hemanth et al [61] Gondal et al [19] Mansour et al [21] Quellec et al [22] Van et al [28] Umapathy et al [30] Diaz et al [39] Gargeya et al [52], Ghosh et al [53] Jiang et al [54] Yang et al [57] Pires et al [60] Singh et al [47] Singh et al [48] Legend…”

Section: Messidor-2mentioning

confidence: 99%

“…Cataract Finally, cataract detection using DL was performed by Pratap et al [50]. Authors have collected data from various sources such as HRF, STARE [80], DIARETDB0, MESSIDOR [77], FIRE, etc.…”

Section: A DL Approaches Employing Tlmentioning

confidence: 99%

Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey

et al. 2020

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Diabetes Mellitus, or Diabetes, is a disease in which a person's body fails to respond to insulin released by their pancreas, or it does not produce sufficient insulin. People suffering from diabetes are at high risk of developing various eye diseases over time. As a result of advances in machine learning techniques, early detection of diabetic eye disease using an automated system brings substantial benefits over manual detection. A variety of advanced studies relating to the detection of diabetic eye disease have recently been published. This paper presents a systematic survey of automated approaches to diabetic eye disease detection from several aspects, namely: i) available datasets, ii) image preprocessing techniques, iii) deep learning models and iv) performance evaluation metrics. The survey provides a comprehensive synopsis of diabetic eye disease detection approaches, including state of the art field approaches, which aim to provide valuable insight into research communities, healthcare professionals and patients with diabetes.

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“…With the rapid advancement of deep learning (DL) in healthcare, it is now possible to perform automated detection of several anterior segment eye diseases, such as pterygium 1 , corneal ulcer 2 , and cataracts using anterior segment photographs 3 . There is an increasing need for evaluation of corneal, glaucoma, and lens disease on DL algorithms with anterior segment imagery in telemedicine approach or clinical practice 4 – 6 .…”

Section: Introductionmentioning

confidence: 99%

Development and validation of deep learning algorithms for automated eye laterality detection with anterior segment photography

Zheng

Xie

Wang

et al. 2021

Sci Rep

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This paper aimed to develop and validate a deep learning (DL) model for automated detection of the laterality of the eye on anterior segment photographs. Anterior segment photographs for training a DL model were collected with the Scheimpflug anterior segment analyzer. We applied transfer learning and fine-tuning of pre-trained deep convolutional neural networks (InceptionV3, VGG16, MobileNetV2) to develop DL models for determining the eye laterality. Testing datasets, from Scheimpflug and slit-lamp digital camera photography, were employed to test the DL model, and the results were compared with a classification performed by human experts. The performance of the DL model was evaluated by accuracy, sensitivity, specificity, operating characteristic curves, and corresponding area under the curve values. A total of 14,468 photographs were collected for the development of DL models. After training for 100 epochs, the DL models of the InceptionV3 mode achieved the area under the receiver operating characteristic curve of 0.998 (with 95% CI 0.924–0.958) for detecting eye laterality. In the external testing dataset (76 primary gaze photographs taken by a digital camera), the DL model achieves an accuracy of 96.1% (95% CI 91.7%–100%), which is better than an accuracy of 72.3% (95% CI 62.2%–82.4%), 82.8% (95% CI 78.7%–86.9%) and 86.8% (95% CI 82.5%–91.1%) achieved by human graders. Our study demonstrated that this high-performing DL model can be used for automated labeling for the laterality of eyes. Our DL model is useful for managing a large volume of the anterior segment images with a slit-lamp camera in the clinical setting.

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Computer-aided diagnosis of cataract using deep transfer learning

Cited by 100 publications

References 24 publications

<p>Identifying Diabetic Macular Edema and Other Retinal Diseases by Optical Coherence Tomography Image and Multiscale Deep Learning</p>

<p>Identifying Diabetic Macular Edema and Other Retinal Diseases by Optical Coherence Tomography Image and Multiscale Deep Learning</p>

Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey

Development and validation of deep learning algorithms for automated eye laterality detection with anterior segment photography

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