Effect of Colored Noise on Neuron Membrane Size Using Stochastic Hodgkin-Huxley Equations

Kako, Najdavan A.; Abdulazeez, Adnan Mohsin; Sadeeq, Haval Tariq

doi:10.1109/iec52205.2021.9476110

Cited by 2 publications

(2 citation statements)

References 36 publications

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“…High-quality annotated data requires the involvement of clinical experts for labeling, which can be costly and time-intensive. Additionally, datasets must encompass a variety of factors, including different races and imaging devices, to enhance the model's generalization capabilities [58], [59]. Insufficient Information in OCT Images: Clinicians typically do not rely solely on OCT images for diagnosis.…”

Section: Challenges In Deep Learning For Oct-based Ophthalmic Disease...mentioning

confidence: 99%

OCT Images Diagnosis Based on Deep Learning – A Review

Abdi,

Abdulazeez

2024

ijcs

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The recent advancements in deep learning technology have significantly transformed the field of medical imaging, namely in the diagnosis of ocular illnesses. The progress made in this field has improved the capacity to extract and evaluate intricate characteristics in images, with Optical Coherence Tomography (OCT) playing a crucial role. OCT has become known for its safe qualities and its high level of detail, rendering it an essential instrument in the diagnosis of eye diseases. The interesting improvement in research is centered around the integration of deep learning with OCT for the purpose of automating the detection of eye diseases. We conducted a comprehensive study that explores several diagnostic methods and the wide-ranging uses of OCT. Additionally, it addresses the accessibility of publicly available datasets that are specifically tailored to optical coherence tomography (OCT). The paper provides a detailed review of the most recent advancements in computer-assisted diagnostic methods for diseases of the eye, such as age-related macular degeneration, glaucoma, and diabetic macular edema, with a particular focus on the effective use of OCT. Moreover, the paper systematically analyzes the primary challenges that deep learning faces in OCT image interpretation, emphasizing the intricate nature and possibilities of this field.

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Section: Challenges In Deep Learning For Oct-based Ophthalmic Disease...mentioning

confidence: 99%

OCT Images Diagnosis Based on Deep Learning – A Review

Abdi,

Abdulazeez

2024

ijcs

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“…The input image in JPEG is divided into 8_8 blocks that are not overlapped, all of which will be translated into block DCT frequency (BDCT). A binary stream is then compressed with DCT coefficients by quantizing and entropy coding for each transformed block [39] [40].…”

Section: Image Formatsmentioning

confidence: 99%

Image Compression Using Neural Networks: A Review

et al. 2021

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Computer images consist of huge data and thus require more memory space. The compressed image requires less memory space and less transmission time. Imaging and video coding technology in recent years has evolved steadily. However, the image data growth rate is far above the compression ratio growth, Considering image and video acquisition system popularization. It is generally accepted, in particular that further improvement of coding efficiency within the conventional hybrid coding system is increasingly challenged. A new and exciting image compression solution is also offered by the deep convolution neural network (CNN), which in recent years has resumed the neural network and achieved significant success both in artificial intelligent fields and in signal processing. In this paper we include a systematic, detailed and current analysis of image compression techniques based on the neural network. Images are applied to the evolution and growth of compression methods based on the neural networks. In particular, the end-to-end frames based on neural networks are reviewed, revealing fascinating explorations of frameworks/standards for next-generation image coding. The most important studies are highlighted and future trends even envisaged in relation to image coding topics using neural networks.

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Effect of Colored Noise on Neuron Membrane Size Using Stochastic Hodgkin-Huxley Equations

Cited by 2 publications

References 36 publications

OCT Images Diagnosis Based on Deep Learning – A Review

OCT Images Diagnosis Based on Deep Learning – A Review

Image Compression Using Neural Networks: A Review

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