Deep learning based search engine for biomedical images using convolutional neural networks

Mishra, Ramesh Kumar; Tripathi, Surya Prakash

doi:10.1007/s11042-020-10391-w

Cited by 6 publications

(3 citation statements)

References 33 publications

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“…The researchers Mishra and Tripathi (2021) proposed the CNN model by training a deep learning model to implement the search engine for biomedical images. Furthermore, for boosting the performance of biomedical search engines, a fusion of DCNN and vector space-based biomedical picture query similarity matching techniques were presented.…”

Section: Custom Convolutional Neural Networkmentioning

confidence: 99%

A Comprehensive Overview of Handwritten Recognition Techniques: A Survey

Ali¹,

Abdulrazzaq²

2023

Journal of Computer Science

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Deep learning and deep neural networks, particularly ConvolutionalNeural Networks (CNNs), are rapidly growing areas of machine learning and are currently the primary tools used for image analysis and classification applications. Handwriting recognition involves using computer algorithms and software to interpret and recognize handwritten text and drawings and has various applications such as automated handwriting analysis, document digitization, and handwriting-based user interfaces. Many deep learning models have been applied in the field of handwriting recognition and various datasets have been used to evaluate new computer vision techniques. This article provides an overview of the current state-of-the-art approaches and contributions to handwriting recognition using different datasets. Furthermore, the paper explains the most commonly used algorithms for recognizing handwritten characters, words, and numbers. Compares them based on their accuracy. This study covered different aspects and methods of machine learning and Deep Learning (DL) for handwritten recognition that showed different achievements for each.

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Section: Custom Convolutional Neural Networkmentioning

confidence: 99%

A Comprehensive Overview of Handwritten Recognition Techniques: A Survey

Ali¹,

Abdulrazzaq²

2023

Journal of Computer Science

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“…(i) e proposed work can be tested for a deep learning approach for microarray cancer data classification [34]; graphology based handwritten character analysis for human behavior identification [35] and a deep neural network-based screening model for COVID-19-infected patients using chest X-ray images [36]. (ii) Also, the work done in this research can also be practiced/verified for the rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images [37], visibility improvement, and mass segmentation of mammogram images using quantile separated histogram equalization with local contrast enhancement [38][39][40].…”

Section: Man-in-middlementioning

confidence: 99%

Secure Patient Authentication Framework in the Healthcare System Using Wireless Medical Sensor Networks

Jan

Ali

Abbasi

et al. 2021

Journal of Healthcare Engineering

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Biosensor is a means to transmit some physical phenomena, like body temperature, pulse, respiratory rate, electroencephalogram (EEG), electrocardiogram (ECG), and blood pressure. Such transmission is performed via Wireless Medical Sensor Network (WMSN) while diagnosing patients remotely through Internet-of-Medical-Things (IoMT). The sensitive data transmitted through WMSN from IoMT over an insecure channel is vulnerable to several threats and needs proper attention to be secured from adversaries. In contrast to addressing the security of all associated entities involving patient monitoring in the healthcare system or ensuring the integrity, authorization, and nonrepudiation of information over the communication line, no one can guarantee its security without a robust authentication protocol. Therefore, we have proposed a lightweight and robust authentication scheme for the network-enabled healthcare devices (IoMT) that mitigate all the identified weaknesses posed in the recent literature. The proposed protocol’s security has been analyzed formally using BAN logic and ProVerif2.02 and informally using pragmatic illustration. Simultaneously, at the end of the paper, the performance analysis result shows a delicate balance of security with performance that is often missing in the current protocols.

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“…Semantic segmentation gives us more detailed understanding of images than image classification [1]- [5] or object detection [6]- [14]. This understanding is crucial in many different domains such as autonomous driving [15]- [17], robotics [18]- [20], image search engines [21]- [23], etc. Recently, many semantic segmentation methods have emerged.…”

Section: Introductionmentioning

confidence: 99%

CSC-Unet: A Novel Convolutional Sparse Coding Strategy Based Neural Network for Semantic Segmentation

Tang,

He,

Yang

et al. 2024

IEEE Access

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It is still a challenging task to perform the semantic segmentation with high accuracy due to the complexity of real picture scenes. Many semantic segmentation methods based on traditional deep learning insufficiently captured the semantic and appearance information of images, which put limit on their generality and robustness for various application scenes. Thus, in this paper, we proposed a novel strategy that reformulated the popularly used convolution operation to multi-layer convolutional sparse coding block in semantic segmentation method to ease the aforementioned deficiency. To prove the effectiveness of our idea, we chose the widely used U-Net model for the demonstration purpose, and we designed CSC-Unet model series based on U-Net. Through extensive analysis and experiments, we provided credible evidence showing that the multi-layer convolutional sparse coding block enables semantic segmentation model to converge faster, extract finer semantic and appearance information of images, and improve the ability to recover spatial detail information. The best CSC-Unet model significantly outperforms the results of the original U-Net on three public datasets with different scenarios, i.e., 87.14% vs. 84.71% on DeepCrack dataset, 68.91% vs. 67.09% on Nuclei dataset, and 53.68% vs. 48.82% on CamVid dataset, respectively. In addition, the proposed strategy could be possibly used to significantly improve segmentation performance of any semantic segmentation model that involves convolution operations and the corresponding code is available at https://github.com/NZWANG/CSC-Unet.

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Deep learning based search engine for biomedical images using convolutional neural networks

Cited by 6 publications

References 33 publications

A Comprehensive Overview of Handwritten Recognition Techniques: A Survey

A Comprehensive Overview of Handwritten Recognition Techniques: A Survey

Secure Patient Authentication Framework in the Healthcare System Using Wireless Medical Sensor Networks

CSC-Unet: A Novel Convolutional Sparse Coding Strategy Based Neural Network for Semantic Segmentation

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