Detecting Cervix Type Using Deep learning and GPU

Bijoy, M. B.; Shilimkar, Vaibhav; Jayaraj, P. B.

doi:10.1109/r10-htc.2018.8629824

Cited by 2 publications

(4 citation statements)

References 6 publications

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“…Bijoy et al 10 presented a deep learning model for cervix-type classification. A deep Convolutional Neural Network (CNN) is built from scratch and trained using a Graphics Processing Unit along with transfer learning models, namely VGG16 and InceptionV3.…”

Section: Related Workmentioning

confidence: 99%

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A customized ConvNeXt‐XL network with fusion of deep and handcrafted features for colposcopy image classification

Natarajan,

Devan,

Palayanoor Seethapathy

et al. 2024

Int J Imaging Syst Tech

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Cervical cancer is the second most frequent cancer among women of all age groups worldwide. It occurs due to human papillomavirus. In the premature stages, the symptoms will not be predominant until they reach the final stage of cancer. Detection and classification of cervical cancer always demand gynecologists with the necessary skills and experience. The goal of the proposed work is to develop a deep learning framework to facilitate the automated classification of cervical cancer using colposcopy images. The following Deep Convolutional Neural Network (DCNN) models are proposed to detect cervical cancer and classify cervix‐type images. (i) the pre‐trained DCNNs, namely VGG16, ResNet50, InceptionV3, InceptionResNetV2, and ConvNeXtXLarge (ConvNeXt‐XL) using Softmax classifier based on deep features; (ii) the ConvNeXt‐XL model with classification using Support Vector Machine (SVM), K Nearest Neighbor (KNN) and Decision Tree (DT) based on deep features; (iii) a customized ConvNeXt‐XL network to enhance the classification accuracy using serially concatenated handcrafted and deep features. The research experiment was carried out separately using two datasets: the Cervix‐Type dataset (Type 1, Type 2, and Type 3) and the Real Time Cervical dataset (Normal and Abnormal). The simulation outcome confirms that the customized ConvNeXt‐XL helped to improve the classification accuracy with the Cervix‐Type dataset (>97%) and Real Time Cervical dataset (>98%).

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Section: Related Workmentioning

confidence: 99%

“…In the existing works, [10][11][12][13][14][15] the cervix-type classification was done using Deep CNN models. The cervix types are classified using the Softmax classifier in the last layer of the pre-trained models.…”

Section: Related Workmentioning

confidence: 99%

A customized ConvNeXt‐XL network with fusion of deep and handcrafted features for colposcopy image classification

Natarajan,

Devan,

Palayanoor Seethapathy

et al. 2024

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…Architecture framework and strategy [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] Scheduling and communication [26][27][28][29] Image processing and computer vision [30][31][32][33][34][35][36][37][38][39][40] Medical or health [41][42][43][44] Modeling or prediction [45][46][47][48][49][50][51] Convolution or performance analysis [6,[52][53][54] VLSI placement [55] GPU-based Machine Learning Technologies for EI Architecture platform [56][57][58][59]…”

Section: Gpu-based Deep Learning Technologies For Eimentioning

confidence: 99%

“…Cervical cancer is a common cancer in women. The authors in [43] proposed an approach to classify the cervix type using DNN and GPU. Their experimental results used TensorFlow and Keras models and techniques and a CUDA parallel computing platform.…”

Section: Medical or Healthmentioning

confidence: 99%

GPU-Based Embedded Intelligence Architectures and Applications

Ang

Seng

2021

Electronics

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This paper present contributions to the state-of-the art for graphics processing unit (GPU-based) embedded intelligence (EI) research for architectures and applications. This paper gives a comprehensive review and representative studies of the emerging and current paradigms for GPU-based EI with the focus on the architecture, technologies and applications: (1) First, the overview and classifications of GPU-based EI research are presented to give the full spectrum in this area that also serves as a concise summary of the scope of the paper; (2) Second, various architecture technologies for GPU-based deep learning techniques and applications are discussed in detail; and (3) Third, various architecture technologies for machine learning techniques and applications are discussed. This paper aims to give useful insights for the research area and motivate researchers towards the development of GPU-based EI for practical deployment and applications.

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Detecting Cervix Type Using Deep learning and GPU

Cited by 2 publications

References 6 publications

A customized ConvNeXt‐XL network with fusion of deep and handcrafted features for colposcopy image classification

A customized ConvNeXt‐XL network with fusion of deep and handcrafted features for colposcopy image classification

GPU-Based Embedded Intelligence Architectures and Applications

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