Automatic Feature Learning to Grade Nuclear Cataracts Based on Deep Learning

Abstract

The proposed method is useful for assisting and improving clinical management of the disease in the context of large-population screening and has the potential to be applied to other eye diseases.

A survey on deep learning in medical image analysis

“…A somewhat similar approach was followed by Kawahara and Hamarneh (2016) who used a multi-stream CNN to classify skin lesions, where each stream works on a different resolution of the image. Gao et al (2015) proposed to use a combination of CNNs and RNNs for grading nuclear cataracts in slit-lamp images, where CNN filters were pre-trained. This combination allows the processing of all contextual information regardless of image size.…”

“…Kisilev et al (2016) used a completely different approach and predicted categorical BI-RADS descriptors for breast lesions. In their work they focused on three descriptors used in mammography: shape, margin, and density, Fu et al (2016a) Blood vessel segmentation; extending the approach by Fu et al (2016b) by reformulating CRF as RNN Mahapatra et al (2016) Image quality assessment; classification output using CNN-based features combined with the output using saliency maps Maninis et al (2016) Segmentation of blood vessels and optic disk; VGG-19 network extended with specialized layers for each segmentation task Wu et al (2016) Blood vessel segmentation; patch-based CNN followed by mapping PCA solution of last layer feature maps to full segmentation Zilly et al (2017) Segmentation of the optic disk and the optic cup; simple CNN with filters sequentially learned using boosting Color fundus images: detection of abnormalities and diseases Chen et al (2015d) Glaucoma detection; end-to-end CNN, the input is a patch centered at the optic disk Abràmoff et al (2016) Diabetic retinopathy detection; end-to-end CNN, outperforms traditional method, evaluated on a public dataset Burlina et al (2016) Age-related macular degeneration detection; uses overfeat pretrained network for feature extraction van Grinsven et al (2016) Hemorrhage detection; CNN dynamically trained using selective data sampling to perform hard negative mining Gulshan et al (2016) Diabetic retinopathy detection; Inception network, performance comparable to a panel of seven certified ophthalmologists Hard exudate detection; end-to-end CNN combined with the outputs of traditional classifiers for detection of landmarks Worrall et al (2016) Retinopathy of prematurity detection; fine-tuned ImageNet trained GoogLeNet, feature map visualization to highlight disease Work in other imaging modalities Gao et al (2015) Cataract classification in slit lamp images; CNN followed by a set of recursive neural networks to extract higher order features Schlegl et al (2015) Fluid segmentation in OCT; weakly supervised CNN improved with semantic descriptors from clinical reports Blood vessel segmentation in OCT angiography; simple CNN, segmentation of several capillary networks where each have their own class label. The system was fed with the image data and region proposals and predicts the correct label for each descriptor (e.g.…”

Automatic Classification of Cataract based on Deep Learnig

“…Gray level image gradient-based features were employed for automatic grading of nuclear cataract [17]. The work in [6] used an deep feature learning approach for grading nuclear cataract. By retro-illumination images, the spoke-like features of cortical opacity were utilized to detect cortical opacities and grade the severity of cortical cataract in [11].…”

A novel training algorithm for convolutional neural network

“…Adaptively learn representation that is more effective for the task of vehicle color recognition using spatial pyramid deep learning is given by Chuanping Hu et al [54]. Deep learning is also been used to grade nuclear cataracts [55]. Deep learning is been widely used in medical image processing for segmentation, classification and registration [56][57][58][59][60][61], image denoising [62] and multimodal learning [63].…”