Deep Residual Learning for Image Classification using Cross Validation

Tripathi, Kshitij; Gupta, Anil Kumar; Vyas, Rajendra G.

doi:10.35940/ijitee.f4131.049620

Cited by 7 publications

(2 citation statements)

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“…CNNs have become the mainstream algorithms for image classification due to their remarkable performance for object detection, action recognition, image classification, segmentation and disease diagnosis [7,[14][15][16][17][18][19][20][21][22]. CNNs have the advantage of being able to distinguish complex shapes of images [23] due to their ability to learn and extract features without the need for prior knowledge or human intervention [24].…”

Section: Basic Architectures Of Cnnsmentioning

confidence: 99%

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

Mohammed,

Tune,

Assefa

et al. 2024

MAKE

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In this review, we compiled convolutional neural network (CNN) methods which have the potential to automate the manual, costly and error-prone processing of medical images. We attempted to provide a thorough survey of improved architectures, popular frameworks, activation functions, ensemble techniques, hyperparameter optimizations, performance metrics, relevant datasets and data preprocessing strategies that can be used to design robust CNN models. We also used machine learning algorithms for the statistical modeling of the current literature to uncover latent topics, method gaps, prevalent themes and potential future advancements. The statistical modeling results indicate a temporal shift in favor of improved CNN designs, such as a shift from the use of a CNN architecture to a CNN-transformer hybrid. The insights from statistical modeling point that the surge of CNN practitioners into the medical imaging field, partly driven by the COVID-19 challenge, catalyzed the use of CNN methods for detecting and diagnosing pathological conditions. This phenomenon likely contributed to the sharp increase in the number of publications on the use of CNNs for medical imaging, both during and after the pandemic. Overall, the existing literature has certain gaps in scope with respect to the design and optimization of CNN architectures and methods specifically for medical imaging. Additionally, there is a lack of post hoc explainability of CNN models and slow progress in adopting CNNs for low-resource medical imaging. This review ends with a list of open research questions that have been identified through statistical modeling and recommendations that can potentially help set up more robust, improved and reproducible CNN experiments for medical imaging.

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Section: Basic Architectures Of Cnnsmentioning

confidence: 99%

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

Mohammed,

Tune,

Assefa

et al. 2024

MAKE

View full text Add to dashboard Cite

show abstract

“…The ResNet-50 architecture was used to fit the training data without transfer learning because the initial weights were derived on a completely different type of data and are therefore unlikely to be of any benefit (Tripathi et al, 2020). ResNet's main features are residual learning and identity mapping.…”

Section: 1mentioning

confidence: 99%

Hybrid deep learning model-based prediction of images related to cyberbullying

Elmezain,

Malki,

Gad

et al. 2022

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Cyberbullying has become more widespread as a result of the common use of social media, particularly among teenagers and young people. A lack of studies on the types of advice and support available to victims of bullying has a negative impact on individuals and society. This work proposes a hybrid model based on transformer models in conjunction with a support vector machine (SVM) to classify our own data set images. First, seven different convolutional neural network architectures are employed to decide which is best in terms of results. Second, feature extraction is performed using four top models, namely, ResNet50, EfficientNetB0, MobileNet and Xception architectures. In addition, each architecture extracts the same number of features as the number of images in the data set, and these features are concatenated. Finally, the features are optimized and then provided as input to the SVM classifier. The accuracy rate of the proposed merged models with the SVM classifier achieved 96.05%. Furthermore, the classification precision of the proposed merged model is 99% in the bullying class and 93% in the non-bullying class. According to these results, bullying has a negative impact on students' academic performance. The results help stakeholders to take necessary measures against bullies and increase the community's awareness of this phenomenon.

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Adv-BDPM: Adversarial attack based on Boundary Diffusion Probability Model

Zhang,

Dong

2023

Neural Networks

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Deep Residual Learning for Image Classification using Cross Validation

Cited by 7 publications

References 0 publications

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

Hybrid deep learning model-based prediction of images related to cyberbullying

Adv-BDPM: Adversarial attack based on Boundary Diffusion Probability Model

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