The histological diagnosis of breast cancer by employing scale invariant ResNet 18 with spatial supervised technique

Bukhari, Syed Usama Khalid; Syed, Asmara; Khalid, Syed Safwan; Shah, Syed Sajid Hussain

doi:10.1145/3492323.3495596

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…While identity mapping maps the input straight to the output, skip connections allow the network to skip over particular layers. After the network has been compressed, many informational aspects are explored [46]. A typical residual block used in the ResNet18 and ResNet34 CNN models is shown in figure 2.…”

Section: Residual Blocksmentioning

confidence: 99%

Investigating the detection of breast cancer with deep transfer learning using ResNet18 and ResNet34

Subaar,

Addai,

Addison

et al. 2024

Biomed. Phys. Eng. Express

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A lot of underdeveloped nations, particularly in Africa, struggle with cancer-related, deadly diseases. Particularly in women, the incidence of breast cancer is rising daily because of ignorance and delayed diagnosis. Only by correctly identifying and diagnosing cancer in its very early stages of development can be effectively treated. The classification of cancer can be accelerated and automated with the aid of computer-aided diagnosis and medical image analysis techniques. This research provides the use of transfer learning from a Residual Network 18 (ResNet18) and Residual Network 34 (Resnet34) architectures to detect breast cancer. The study examined how breast cancer can be identified in breast mammography pictures using transfer learning from ResNet18 and ResNet34, and developed a demo app for radiologists using the trained models with the best validation accuracy. For the multi-class classification of the images, the study gave an average accuracy for binary classification of benign or malignant cancer cases of 86.7% validation accuracy for ResNet34 and 92% validation accuracy for ResNet18. A prototype web application showcasing ResNet18 performance has been created. The acquired results show how transfer learning can improve the accuracy of breast cancer detection, providing invaluable assistance to medical professionals, particularly in an African scenario.

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Section: Residual Blocksmentioning

confidence: 99%

Investigating the detection of breast cancer with deep transfer learning using ResNet18 and ResNet34

Subaar,

Addai,

Addison

et al. 2024

Biomed. Phys. Eng. Express

View full text Add to dashboard Cite

show abstract

“…At present, the mainstream classification algorithms, such as VGG 15,16 and ResNet, 17,18 exhibit high accuracy for object classification but are unsuitable for clinical practice due to slow detection speeds. The MobileNetV2 is a lightweight CNN network, which uses deep separable convolution to achieve end‐to‐end object classification 19,20 .…”

Section: Introductionmentioning

confidence: 99%

A lightweight hybrid model for the automatic recognition of uterine fibroid ultrasound images based on deep learning

Cai,

Yang,

Xie

et al. 2024

J of Clinical Ultrasound

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PurposeUterine fibroids (UF) are the most frequent tumors in ladies and can pose an enormous threat to complications, such as miscarriage. The accuracy of prognosis may also be affected by way of doctor inexperience and fatigue, underscoring the want for automatic classification fashions that can analyze UF from a giant wide variety of images.MethodsA hybrid model has been proposed that combines the MobileNetV2 community and deep convolutional generative adversarial networks (DCGAN) into useful resources for medical practitioners in figuring out UF and evaluating its characteristics. Real‐time automated classification of UF can aid in diagnosing the circumstance and minimizing subjective errors. The DCGAN science is utilized for superior statistics augmentation to create first‐rate UF images, which are labeled into UF and non‐uterine‐fibroid (NUF) classes. The MobileNetV2 model then precisely classifies the photos based totally on this data.ResultsThe overall performance of the hybrid model contrasts with different models. The hybrid model achieves a real‐time classification velocity of 40 frames per second (FPS), an accuracy of 97.45%, and an F1 rating of 0.9741.ConclusionBy using this deep learning hybrid approach, we address the shortcomings of the current classification methods of uterine fibroid.

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The histological diagnosis of breast cancer by employing scale invariant ResNet 18 with spatial supervised technique

Cited by 2 publications

References 7 publications

Investigating the detection of breast cancer with deep transfer learning using ResNet18 and ResNet34

Investigating the detection of breast cancer with deep transfer learning using ResNet18 and ResNet34

A lightweight hybrid model for the automatic recognition of uterine fibroid ultrasound images based on deep learning

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