Efficient deep CNN-based gender classification using Iris wavelet scattering

Aryanmehr, Saeed; Boroujeni, Farsad Zamani

doi:10.1007/s11042-022-14062-w

Cited by 4 publications

(7 citation statements)

References 42 publications

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“…Wavelet scattering transform can be extended to provide rotation invariance, making it adaptable to a wider range of textural variations. Hierarchical features: the transform provides a hierarchical representation of the image, capturing both low‐level and high‐level features. This hierarchical feature representation can be particularly valuable for pattern classification tasks where different levels of abstraction are required. State‐of‐the‐art performance: empirical studies have shown that wavelet scattering transform often outperforms traditional texture feature extraction methods, such as gray‐level co‐occurrence matrix and local binary patterns, in various pattern classification tasks 57–59 …”

Section: Methodsmentioning

confidence: 99%

Wavelet scattering networks in deep learning for discovering protein markers in a cohort of Swedish rectal cancer patients

Pham,

Sun

2023

Cancer Medicine

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BackgroundCancer biomarkers play a pivotal role in the diagnosis, prognosis, and treatment response prediction of the disease. In this study, we analyzed the expression levels of RhoB and DNp73 proteins in rectal cancer, as captured in immunohistochemical images, to predict the 5‐year survival time of two patient groups: one with preoperative radiotherapy and one without.MethodsThe utilization of deep convolutional neural networks in medical research, particularly in clinical cancer studies, has been gaining substantial attention. This success primarily stems from their ability to extract intricate image features that prove invaluable in machine learning. Another innovative method for extracting features at multiple levels is the wavelet‐scattering network. Our study combines the strengths of these two convolution‐based approaches to robustly extract image features related to protein expression.ResultsThe efficacy of our approach was evaluated across various tissue types, including tumor, biopsy, metastasis, and adjacent normal tissue. Statistical assessments demonstrated exceptional performance across a range of metrics, including prediction accuracy, classification accuracy, precision, and the area under the receiver operating characteristic curve.ConclusionThese results underscore the potential of dual convolutional learning to assist clinical researchers in the timely validation and discovery of cancer biomarkers.

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Section: Methodsmentioning

confidence: 99%

Wavelet scattering networks in deep learning for discovering protein markers in a cohort of Swedish rectal cancer patients

Pham,

Sun

2023

Cancer Medicine

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“…34,35 Compared with standard wavelet transforms, wavelet image scattering constructs low-variance image representations that are insensitive to time and frequency deformations. [36][37][38] An advantage of WST over CNN-based F I G U R E 1 Leishman stained 100Â peripheral blood smear images of dengue-infected patients (left) and healthy subjects (right). In the former, the lymphocyte appears larger with a bigger, irregular nucleus and abundant cytoplasm.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Hence, WST is preferred for limited training datasets. 38 There is a paucity of studies on the use of WST-based feature extraction in dengue diagnostic models. In their glaucoma diagnostic model, Agboola et al 39 stage-wise decomposed retinal fundus images by inputting them into a wavelet scattering network constructed in MATLAB.…”

Section: Literature Reviewmentioning

confidence: 99%

“…WST is a wavelet transform‐based method that extracts features in both time and frequency domains 34,35 . Compared with standard wavelet transforms, wavelet image scattering constructs low‐variance image representations that are insensitive to time and frequency deformations 36–38 . An advantage of WST over CNN‐based methods is its use of fixed and preset wavelet filters, which obviates the need for high‐volume data samples to train filter responses.…”

Section: Introductionmentioning

confidence: 99%

“…An advantage of WST over CNN‐based methods is its use of fixed and preset wavelet filters, which obviates the need for high‐volume data samples to train filter responses. Hence, WST is preferred for limited training datasets 38 . There is a paucity of studies on the use of WST‐based feature extraction in dengue diagnostic models.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Wavelet scattering‐ and object detection‐based computer vision for identifying dengue from peripheral blood microscopy

Dsilva,

Tantri,

Sampathila

et al. 2024

Int J Imaging Syst Tech

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Dengue fever infection is a global health concern. Early disease detection is crucial for averting complications and fatality. Characteristic morphological changes in lymphocytes can be observed on a peripheral blood smear (PBS) in cases of dengue infection. In this research, we have developed automated computer vision models for dengue detection on PBS images using two approaches: wavelet scattering transform (WST)‐based feature engineering and classification and You Only Look Once (YOLO)‐based deep transfer learning for object detection. In the former, Morlet wavelet scattering features extracted from lymphocytes were used as input for five shallow classifiers for image classification. Among these, the support vector machine achieved the best results of 98.7% accuracy using 10‐fold cross‐validation. In the latter, computer vision‐enabled object detection was implemented using five YOLOv8 scaled variants. Among these, YOLOv8s and YOLOv8l attained identical best mean accuracy of 99.3% ± 1.4% across five independent experiments. Our results confirmed the feasibility and excellent diagnostic accuracy for both WST‐ and YOLOv8‐enabled computer vision approaches for diagnosing dengue infection in PBS images. This research incorporates deep machine learning along with AI technology to enhance understanding and capabilities in automated Dengue diagnosis. The significance of this research extends to the broader domain of mosquito‐borne illnesses. However, it is important to note that the findings are limited to the dataset used by the researchers.

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Deep hashing with multilayer CNN-based biometric authentication for identifying individuals in transportation security

Borra,

Premalatha,

Divya

et al. 2024

J Transp Secur

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Efficient deep CNN-based gender classification using Iris wavelet scattering

Cited by 4 publications

References 42 publications

Wavelet scattering networks in deep learning for discovering protein markers in a cohort of Swedish rectal cancer patients

Wavelet scattering networks in deep learning for discovering protein markers in a cohort of Swedish rectal cancer patients

Wavelet scattering‐ and object detection‐based computer vision for identifying dengue from peripheral blood microscopy

Deep hashing with multilayer CNN-based biometric authentication for identifying individuals in transportation security

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