Saliency-Driven System With Deep Learning for Cell Image Classification

Ferreira, Daniel Silva; Geraldo, L B Ramalho; Andrea, G C Bianchi; Carneiro, Cláudia Martins; Ushizima, Daniela

doi:10.1109/isbi.2019.8759398

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…Many researchers utilize CNN to recognize cells in cryo-EM pictures and TEM images. Researchers looked at single-particle recognition on cryo-EM photos [ 10 ]. DeepEM is a software framework that they built.…”

Section: Related Workmentioning

confidence: 99%

Biomedical Microscopic Imaging in Computational Intelligence Using Deep Learning Ensemble Convolution Learning-Based Feature Extraction and Classification

Vivek

Naureen²,

Ashraf³

et al. 2022

Computational Intelligence and Neuroscience

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Microscopy image analysis gives quantitative support for enhancing the characterizations of various diseases, including breast cancer, lung cancer, and brain tumors. As a result, it is crucial in computer-assisted diagnosis and prognosis. Understanding the biological principles underlying these dynamic image sequences often necessitates precise analysis and statistical quantification, a major discipline issue. Deep learning methods are increasingly used in bioimage processing as they grow rapidly. This research proposes novel biomedical microscopic image analysis techniques using deep learning architectures based on feature extraction and classification. Here, the input image has been taken as microscopic image, and it has been processed and analyzed for noise removal, edge smoothening, and normalization. The processed image has been extracted based on their features in microscopic image analysis using ConVol_NN architecture with AlexNet model. Then, the features have been classified using ensemble of Inception-ResNet and VGG-16 (EN_InResNet_VGG-16) architectures. The experimental results show various dataset analyses in terms of accuracy of 98%, precision of 90%, computational time of 79%, SNR of 89%, and MSE of 62%.

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Section: Related Workmentioning

confidence: 99%

Biomedical Microscopic Imaging in Computational Intelligence Using Deep Learning Ensemble Convolution Learning-Based Feature Extraction and Classification

Vivek

Naureen²,

Ashraf³

et al. 2022

Computational Intelligence and Neuroscience

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Global-Local Attention Network for Weakly Supervised Cervical Cytology ROI Analysis

Shi

Zheng

et al. 2022

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)

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Prediction of Neurological Disorders Using Visual Saliency

Sreelakshmi

Anoop

2021

Advances in Medical Technologies and Clinical Practice

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Neurological disorders are diseases of the central and peripheral nervous system and most commonly affect middle- or old-age people. Accurate classification and early-stage prediction of such disorders are very crucial for prompt diagnosis and treatment. This chapter discusses a new framework that uses image processing techniques for detecting neurological disorders so that clinicians prevent irreversible changes that may occur in the brain. The newly proposed framework ensures reliable and accurate machine learning techniques using visual saliency algorithms to process brain magnetic resonance imaging (MRI). The authors also provide ample hints and dimensions for the researchers interested in using visual saliency features for disease prediction and detection.

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Saliency-Driven System With Deep Learning for Cell Image Classification

Cited by 3 publications

References 6 publications

Biomedical Microscopic Imaging in Computational Intelligence Using Deep Learning Ensemble Convolution Learning-Based Feature Extraction and Classification

Biomedical Microscopic Imaging in Computational Intelligence Using Deep Learning Ensemble Convolution Learning-Based Feature Extraction and Classification

Global-Local Attention Network for Weakly Supervised Cervical Cytology ROI Analysis

Prediction of Neurological Disorders Using Visual Saliency

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