A literature review: various learning techniques and its applications for eye disease identification using retinal images

Rajyaguru, Vipul C.; Vithalani, C. H.; Thanki, Rohit M.

doi:10.1007/s41870-020-00442-8

Cited by 16 publications

(3 citation statements)

References 40 publications

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“…The structure of a DNN approach is composed of input, hidden, dropout, and output layers ( Rajyaguru, Vithalani & Thanki, 2020 ). The deep neural network is an evolution of the neural network (see Fig.…”

Section: Methodsmentioning

confidence: 99%

Data augmentation and deep neural networks for the classification of Pakistani racial speakers recognition

Amjad

Khan

Chang

2022

PeerJ Computer Science

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Speech emotion recognition (SER) systems have evolved into an important method for recognizing a person in several applications, including e-commerce, everyday interactions, law enforcement, and forensics. The SER system’s efficiency depends on the length of the audio samples used for testing and training. However, the different suggested models successfully obtained relatively high accuracy in this study. Moreover, the degree of SER efficiency is not yet optimum due to the limited database, resulting in overfitting and skewing samples. Therefore, the proposed approach presents a data augmentation method that shifts the pitch, uses multiple window sizes, stretches the time, and adds white noise to the original audio. In addition, a deep model is further evaluated to generate a new paradigm for SER. The data augmentation approach increased the limited amount of data from the Pakistani racial speaker speech dataset in the proposed system. The seven-layer framework was employed to provide the most optimal performance in terms of accuracy compared to other multilayer approaches. The seven-layer method is used in existing works to achieve a very high level of accuracy. The suggested system achieved 97.32% accuracy with a 0.032% loss in the 75%:25% splitting ratio. In addition, more than 500 augmentation data samples were added. Therefore, the proposed approach results show that deep neural networks with data augmentation can enhance the SER performance on the Pakistani racial speech dataset.

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Section: Methodsmentioning

confidence: 99%

Data augmentation and deep neural networks for the classification of Pakistani racial speakers recognition

Amjad

Khan

Chang

2022

PeerJ Computer Science

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“…A general architecture for storing diagnostic data in an international database will let machine learning algorithms more easily predict the diagnosis of diseases based on symptoms. [5]…”

Section: IImentioning

confidence: 99%

Eye Diseases Detection using ML

Pol,

Waghmare,

Shinde

et al. 2024

IJRASET

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This paper presents a systematic recent advancement in the application of machine learning (ML) techniques for the detection of eye diseases. With the increasing prevalence of ocular conditions worldwide, there is a growing need for automated and accurate diagnostic tools to assist clinicians in early detection and intervention. Early detection of eye diseases is critical, as individuals who have a higher risk of developing eye diseases include those with diabetes, those over 60, those with a family history of eye diseases, and those who have had eye surgery or injuries. Treatment of eye diseases and the avoidance of irreversible vision loss depends heavily on early detection and prompt intervention. The prevention or deceleration of vision loss and blindness is largely dependent on the early detection of eye diseases. Many eye diseases, such as glaucoma, cataracts, and diabetic retinopathy, unfortunately, have no early warning signs or symptoms.

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“…Many research works have proposed algorithms and methods to diagnose diseases based on clinical signs and symptoms and apply information technology and health. In addition, many applications support and care for patients’ health to improve life expectancy and health care, such as research in [ 5 – 9 ]. Furthermore, Deep Learning (DL) and Machine Learning (ML) approaches are also used in medicine, such as: making clinical diagnoses, assessing disease status, detecting rare diseases, etc.…”

Section: Introductionmentioning

confidence: 99%

Deep bidirectional LSTM for disease classification supporting hospital admission based on pre-diagnosis: a case study in Vietnam

Nguyen

Pham

et al. 2023

Int. j. inf. tecnol.

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Overcrowding in hospitals in Vietnam has caused many disadvantages in receiving and treating patients. Especially at the stage of receiving and diagnosing procedures taking patients to the treatment departments in the hospital takes up much time. This study proposes a text-based disease diagnosis using text processing techniques (such as Bag of Words, Term Frequency- Inverse Document Frequency, and Tokenizer) combined with classifiers (such as Random Forests (RF), Multi-Layer Perceptron (MLP), Embeddings and Bidirectional Long Short-term memory (LSTM)) on symptoms. As observed from the results, deep Bidirectional LSTM can reach 0.982 in AUC in the classification of 10 diseases on 230,457 samples of pre-diagnosis collected from Vietnam hospitals used in the training and testing phases. The proposed approach is expected to provide a way to automate patient flow in hospitals to improve healthcare in the future.

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A literature review: various learning techniques and its applications for eye disease identification using retinal images

Cited by 16 publications

References 40 publications

Data augmentation and deep neural networks for the classification of Pakistani racial speakers recognition

Data augmentation and deep neural networks for the classification of Pakistani racial speakers recognition

Eye Diseases Detection using ML

Deep bidirectional LSTM for disease classification supporting hospital admission based on pre-diagnosis: a case study in Vietnam

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