FECNet: a Neural Network and a Mobile App for COVID-19 Recognition

Zhang, Yudong; Govindaraj, Vishnuvarthanan; Zhu, Ziquan

doi:10.1007/s11036-023-02140-8

Cited by 9 publications

(1 citation statement)

References 36 publications

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“…Data augmentation serves as a strategic approach to counter overfitting 42 The used MobileNetsV3 model demonstrates enhanced accuracy over its predecessor, the used MobileNetV2 model is attributed to the integration of the SE block and the h-swish activation function. the used EfficientNetB0 model excels in image classification and is preferred for transfer learning.…”

Section: Data Augmentationmentioning

confidence: 99%

Fine tuning deep learning models for breast tumor classification

Heikal,

El-Ghamry,

Elmougy

et al. 2024

Sci Rep

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This paper proposes an approach to enhance the differentiation task between benign and malignant Breast Tumors (BT) using histopathology images from the BreakHis dataset. The main stages involve preprocessing, which encompasses image resizing, data partitioning (training and testing sets), followed by data augmentation techniques. Both feature extraction and classification tasks are employed by a Custom CNN. The experimental results show that the proposed approach using the Custom CNN model exhibits better performance with an accuracy of 84% than applying the same approach using other pretrained models, including MobileNetV3, EfficientNetB0, Vgg16, and ResNet50V2, that present relatively lower accuracies, ranging from 74 to 82%; these four models are used as both feature extractors and classifiers. To increase the accuracy and other performance metrics, Grey Wolf Optimization (GWO), and Modified Gorilla Troops Optimization (MGTO) metaheuristic optimizers are applied to each model separately for hyperparameter tuning. In this case, the experimental results show that the Custom CNN model, refined with MGTO optimization, reaches an exceptional accuracy of 93.13% in just 10 iterations, outperforming the other state-of-the-art methods, and the other four used pretrained models based on the BreakHis dataset.

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Section: Data Augmentationmentioning

confidence: 99%