A Versatile Ultra-Wideband Radio Sensor for Early Stage Detection of Breast Cancer

Sharma, Taruna; Vasishath, Munish; Vasisht, Parikshit; Khan, Amber; Uddin, Moin; Yaduvanshi, Rajveer S.

doi:10.1007/s12647-021-00504-7

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…In its most basic form, it can be explained as uncontrolled or unregulated division of cells. Therefore, it can happen in any portion of the body, such as mouth, throat, stomach, brain, or breast [3][4][5] etc. In this day and age, brain cancer, which is also referred to as Brain Tumor, is the type of cancer that is most susceptible to being cured [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Designing efficient brain tumor classifier using hybrid EfficientNet-faster R-CNN deep learning model

Kharb,

Chaudhary

2024

Eng. Res. Express

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Purpose: Brain tumors have historically posed a significant risk to individuals, potentially leading to fatality. However, in contemporary times, they have emerged as one of the most dangerous health issues affecting both children and adults. The effects of this phenomenon result in the unregulated proliferation of brain cells. Within the field of biological image processing, the utilisation of deep learning algorithms offers a distinct and unparalleled experience. The Convolutional Neural Network (CNN) is a pivotal component in brain tumor categorization applications. However, a notable limitation of CNNs is their extensive execution time, primarily attributed to the substantial number of trainable parameters involved. Methods: This study proposes a hybrid model for brain tumor classification that combines faster R-CNN and EfficientNet. The objective is to minimise the time required for classification without compromising accuracy. The data collection from Figshare was employed during the model's development.Results and Conclusion: The hybrid model proposed in this study demonstrates a notable accuracy of 98.96% during the training phase and 99.2% during the testing phase, surpassing the performance of both the EfficientNet model and the Faster R-CNN model when employed individually. The aforementioned findings were derived through a comparative analysis of the Hybrid model, the EfficientNet model, and the Faster R-CNN model.

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Section: Introductionmentioning

confidence: 99%

Designing efficient brain tumor classifier using hybrid EfficientNet-faster R-CNN deep learning model

Kharb,

Chaudhary

2024

Eng. Res. Express

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Monostatic Radar-Based Microwave Imaging of Breast Tumor Using an Ultra-wideband Dielectric Resonator Antenna (DRA) with a Sierpinski Fractal Defected Ground Structure

Kaur

2022

MAPAN

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A Versatile Ultra-Wideband Radio Sensor for Early Stage Detection of Breast Cancer

Cited by 2 publications

References 34 publications

Designing efficient brain tumor classifier using hybrid EfficientNet-faster R-CNN deep learning model

Designing efficient brain tumor classifier using hybrid EfficientNet-faster R-CNN deep learning model

Monostatic Radar-Based Microwave Imaging of Breast Tumor Using an Ultra-wideband Dielectric Resonator Antenna (DRA) with a Sierpinski Fractal Defected Ground Structure

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