DeepCervix: A deep learning-based framework for the classification of cervical cells using hybrid deep feature fusion techniques

Rahaman, Md Mamunur; Li, Chen; Yao, Yu-Dong; Kulwa, Frank; Wu, Xiangchen; Li, Xiaoyan; Wang, Qian

doi:10.1016/j.compbiomed.2021.104649

Cited by 171 publications

(47 citation statements)

References 56 publications

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“…In recent years, with the maturity of deep learning technology, machine learning has gradually been applied in various areas of the medical field, such as precise cell classification [ 10 ] and image analysis for pathology [ 11 , 12 ]. In addition, human pose estimation has broad application prospects in computer vision, pattern recognition, video/image sequence processing, and other technologies [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis of Gait Characteristics of Patients with Knee Arthritis Based on Human Posture Estimation

Chen

et al. 2022

BioMed Research International

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A gait feature analysis method based on AlphaPose human pose estimation fused with sample entropy is proposed to address complicated, high-cost, and time-consuming postoperative rehabilitation of patients with joint diseases. First, TensorRT was used to optimize the inference of AlphaPose, which consists of the target detection algorithm YOLOv3 and the pose estimation algorithm. It can speed up latency and throughput by about 2.5 times while maintaining the algorithm’s accuracy. Second, the optimized human posture estimation algorithm AlphaPose_trt was used to process gait videos of healthy people and patients with knee arthritis. The joint point motion trajectories of the two groups were extracted, and the sample entropy algorithm quantified the joint trajectory signals for feature analysis. The experimental results showed significant differences in the entropy of the heel and ankle joint motion signals between healthy people and arthritic patients ( p < 0.01 ), which can be used to identify patients with knee arthritis. This technique can assist doctors in determining needed postoperative joint surgery rehabilitation.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis of Gait Characteristics of Patients with Knee Arthritis Based on Human Posture Estimation

Chen

et al. 2022

BioMed Research International

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“… 2020 ), cytopathological image analysis (Rahaman et al. 2021 , 2020 ; Li et al. 2017 ), video analysis (Chen et al.…”

Section: Methodology Analysismentioning

confidence: 99%

A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches

Rahaman

et al. 2022

Artif Intell Rev

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Microorganisms play a vital role in human life. Therefore, microorganism detection is of great significance to human beings. However, the traditional manual microscopic detection methods have the disadvantages of long detection cycle, low detection accuracy in large orders, and great difficulty in detecting uncommon microorganisms. Therefore, it is meaningful to apply computer image analysis technology to the field of microorganism detection. Computer image analysis can realize high-precision and high-efficiency detection of microorganisms. In this review, first,we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. In the end, the future development direction and challenges of microorganism detection are discussed. In general, we have summarized 142 related technical papers from 1985 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of microorganism detection and provide a reference for researchers in other fields.

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“…Its accuracy (98.37%), sensitivity (99.80%), specificity (99.60%), and F -measure (99.80%) were all better than those of the existing method ( 55 ). In addition, hybrid deep feature fusion techniques were proposed, with high accuracy in the SIPAKMeD dataset ( 56 ).…”

Section: Applications Of Ai In Early Screening Of Cervical Cancermentioning

confidence: 99%

Artificial Intelligence in Cervical Cancer Screening and Diagnosis

Hou

Shen

Zhou³

et al. 2022

Front. Oncol.

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Cervical cancer remains a leading cause of cancer death in women, seriously threatening their physical and mental health. It is an easily preventable cancer with early screening and diagnosis. Although technical advancements have significantly improved the early diagnosis of cervical cancer, accurate diagnosis remains difficult owing to various factors. In recent years, artificial intelligence (AI)-based medical diagnostic applications have been on the rise and have excellent applicability in the screening and diagnosis of cervical cancer. Their benefits include reduced time consumption, reduced need for professional and technical personnel, and no bias owing to subjective factors. We, thus, aimed to discuss how AI can be used in cervical cancer screening and diagnosis, particularly to improve the accuracy of early diagnosis. The application and challenges of using AI in the diagnosis and treatment of cervical cancer are also discussed.

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DeepCervix: A deep learning-based framework for the classification of cervical cells using hybrid deep feature fusion techniques

Cited by 171 publications

References 56 publications

[Retracted] Analysis of Gait Characteristics of Patients with Knee Arthritis Based on Human Posture Estimation

[Retracted] Analysis of Gait Characteristics of Patients with Knee Arthritis Based on Human Posture Estimation

A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches

Artificial Intelligence in Cervical Cancer Screening and Diagnosis

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