Microaneurysm detection using fully convolutional neural networks

Chudzik, Piotr; Majumdar, Somshubra; Calivá, Francesco; Al-Diri, Bashir; Hunter, Andrew

doi:10.1016/j.cmpb.2018.02.016

Cited by 170 publications

(85 citation statements)

References 28 publications

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“…Different CNN structures have been developed to solve image processing, pattern recognition, classification, and other problems. Recently, CNNs have been used for facial recognition (Lawrence, Giles, Ah Chung, & Back, 1997), handwritten character classification (Ciresan, Meier, Gambardella, & Schmidhuber, 2011), visual document analysis (Simard, Steinkraus, & Platt, 2003), face sketch synthesis (Jiao, Zhang, Li, Liu, & Ma, 2018), microaneurysm detection (Chudzik, Majumdar, Calivá, Al-Diri, & Hunter, 2018), fingerprint enhancement (Li, Feng, & Kuo, 2018), the segmentation of glioma tumours in brains (Hussain, Anwar, & Majid, 2018), handwriting recognition (Baldominos, Saez, & Isasi, 2018), granite tile classification (Ferreira & Giraldi, 2017), segmenting the neuroanatomy (Wachinger, Reuter, & Klein, 2018), change detection using heterogeneous optical and radar images (Liu, Gong, Qin, & Zhang, 2018), predicting eye fixations (Liu, Han, Liu, & Li, 2018), improving acoustic source localization in noisy and reverberant conditions (Salvati, Drioli, & Foresti, 2018), chest disease detection (Abiyev & Ma'aitah, 2018), short-term wind speed forecasting (Khodayar, Kaynak, & Khodayar, 2017), natural language processing (Kalchbrenner, Grefenstette, & Blunsom, 2014), and image and video recognition problems (Karpathy et al, 2014) and showed good results.…”

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confidence: 99%

Head mouse control system for people with disabilities

Abiyev

Arslan

2019

Expert Systems

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In this paper, a human–machine interface for disabled people with spinal cord injuries is proposed. The designed human–machine interface is an assistive system that uses head movements and blinking for mouse control. In the proposed system, by moving one's head, the user moves the mouse pointer to the required coordinates and then blinks to send commands. The considered head mouse control is based on image processing including facial recognition, in particular, the recognition of the eyes, mouth, and nose. The proposed recognition system is based on the convolutional neural network, which uses the low‐quality images that are captured by a computer's camera. The convolutional neural network (CNN) includes convolutional layers, a pooling layer, and a fully connected network. The CNN transforms the head movements to the actual coordinates of the mouse. The designed system allows people with disabilities to control a mouse pointer with head movements and to control mouse buttons with blinks. The results of the experiments demonstrate that this system is robust and accurate. This invention allows people with disabilities to freely control mouse cursors and mouse buttons without wearing any equipment.

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confidence: 99%

Head mouse control system for people with disabilities

Abiyev

Arslan

2019

Expert Systems

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“…A three-phase MA detection framework is pro-posed by Piotr Chudzik et al [31] by utilizing fully Convolution neural network. Besides how knowledge transfer between the small data set for recognition of MA is shown.…”

Section: H Deep Learning Methodsmentioning

confidence: 99%

Detection of Lesions for Diabetic Retinopathy By using Machine Learning Algorithms

Jebran P*,

Gupta

2020

IJRTE

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In this paper existing writing for computer added diagnosis (CAD) based identification of lesions that might be connected in the early finding of Diabetic Retinopathy (DR) is talked about. The recognition of sores, for example, Microaneurysms (MA), Hemorrhages (HEM) and Exudates (EX) are incorporated in this paper. A range of methodologies starting from conventional morphology to deep learning techniques have been discussed. The different strategies like hand crafted feature extraction to automated CNN based component extraction, single lesion identification to multi sore recognition have been explored. The different stages in each methods beginning from the image preprocessing to classification stage are investigated. The exhibition of the proposed strategies are outlined by various performance measurement parameters and their used data sets are tabulated. Toward the end we examined the future headings.

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“…It can be seen that the proposed method has better performance on MA detection compared to existing algorithms. Especially, as to those using deep learning techniques, the algorithms proposed by Eftekhari et al [8] and Chudzik et al [31] performed very well on FROC score, using two-step CNN and FCN, respectively. It is known that deep learning with CNN for classification has a better performance on accuracy but more computation time for training, requirement of a large number In DIARETDB1 database, MA detection results corresponding to different confidences are shown in different colored circles in Fig.…”

Section: Computation Timementioning

confidence: 99%

“…As deep learning is an emerging computer vision application in medical image processing and proving to be of great help to mankind in machine learning [15], several MA detection methods based on convolutional neural networks (CNN) [8,[29][30][31][32] were proposed. The main limitation of CNN is the requirement of larger training time [15].…”

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Microaneurysms detection in color fundus images using machine learning based on directional local contrast

Long

Chen

et al. 2020

BioMed Eng OnLine

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Background: As one of the majorcomplications of diabetes, diabetic retinopathy (DR) is a leadingcause of visual impairment and blindness due to delayed diagnosisand intervention. Microaneurysms appear as the earliest symptom ofDR. Accurate and reliable detection of microaneurysms in colorfundus images has great importance for DR screening.Methods: A microaneurysms' detection methodusing machine learning based on directional local contrast (DLC) isproposed for the early diagnosis of DR. First, blood vessels wereenhanced and segmented using improved enhancement function based onanalyzing eigenvalues of Hessian matrix. Next, with blood vesselsexcluded, microaneurysm candidate regions were obtained using shapecharacteristics and connected components analysis. After imagesegmented to patches, the features of each microaneurysm candidatepatch were extracted, and each candidate patch was classified intomicroaneurysm or non-microaneurysm. The main contributions of ourstudy are (1) making use of directional local contrast inmicroaneurysms' detection for the first time, which does make sensefor better microaneurysms' classification.(2) Applying threedifferent machine learning techniques for classification andcomparing their performance for microaneurysms' detection. Theproposed algorithm was trained and tested on e-ophtha MA database,and further tested on another independent DIARETDB1 database.Results of microaneurysms' detection on the two databases wereevaluated on lesion level and compared with existing algorithms. Results:The proposed method has achieved better performance compared with existing algorithms on accuracy and computation time. On e-ophtha MA and DIARETDB1 databases, the area under curve (AUC) of receiver operating characteristic (ROC) curve was 0.87 and 0.86, respectively. The free-response ROC (FROC) score on the two databases was 0.374 and 0.210, respectively. The computation time per image with resolution of 2544×1969, 1400×960 and 1500×1152 is 29 s, 3 s and 2.6 s, respectively. Conclusions:The proposed methodusing machine learning based on directional local contrast of imagepatches can effectively detect microaneurysms in color fundus imagesand provide an effective scientific basis for early clinical DRdiagnosis.

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Microaneurysm detection using fully convolutional neural networks

Abstract: Performance, simplicity, and robustness of the proposed method demonstrates its suitability for diabetic retinopathy screening applications.

Cited by 170 publications

References 28 publications

Head mouse control system for people with disabilities

Head mouse control system for people with disabilities

Detection of Lesions for Diabetic Retinopathy By using Machine Learning Algorithms

Microaneurysms detection in color fundus images using machine learning based on directional local contrast

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