Deep integrated pipeline of segmentation guided classification of breast cancer from ultrasound images

Inan, Muhammad Sakib Khan; Alam, Fahim Irfan; Hasan, Rizwan

doi:10.1016/j.bspc.2022.103553

Cited by 43 publications

(17 citation statements)

References 40 publications

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“…The use of image instant to extract information from images has indeed been demonstrated to be useful in a multitude of conditions. Combining orthogonal quaternion harmonic transition events with picture display and attribute selection ideal condition, for instance, is successful in classifying color galaxy photos [13] .…”

Section: Related Workmentioning

confidence: 99%

An intelligent deep convolutional network based COVID-19 detection from chest X-rays

Alshahrni

Ahmad

Abdullah

et al. 2023

Alexandria Engineering Journal

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Section: Related Workmentioning

confidence: 99%

An intelligent deep convolutional network based COVID-19 detection from chest X-rays

Alshahrni

Ahmad

Abdullah

et al. 2023

Alexandria Engineering Journal

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“…Once the feature map is processed, the pooling layer reduces the amount of information contained in order to eliminate redundant information; finally, the output of the pooling layer goes to the fully connected layer to be classified. In this sense, several works [ 143 , 144 , 145 , 146 , 147 , 148 ], have been employed CNN to detect benign and malignant tissues in either mammography or MRI images. They note that the depth of the network, i.e., the number of layers, the fine-tuning of some of the kernel or pooling layers, as well as the number of images, affect the classifier performance.…”

Section: Image Processing and Classification Strategiesmentioning

confidence: 99%

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

Basurto-Hurtado

Cruz-Albarran

Toledano‐Ayala

et al. 2022

Cancers

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Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.

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“…We review briefly deep learning approaches: transfer learning, ResNet, Mobilnet, U-Net, imaging with CNN, and fine-tuned VGG-Net. Several deep-learning models have been proposed, such as AlexNet [10], VGG [11],DenseNet [21] and Residual network [24], and they all have excellent performance in numerous areas. But the Fully Convolutional Networks (FCN) [18] provide superior performance when compared with other deep-learning models with respect to semantic medical image segmentation.…”

Section: Related Workmentioning

confidence: 99%

Hybrid UNET Model Segmentation for an Early Breast Cancer Detection using Ulrasound Images

AHMED¹

2022

Preprint

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. In this paper, we present a deep learning framework built on the U-Net architecture. We used a MobileNetV2 and a VGG16 model encoder to handle the semantic segmentation of a biomedical image effectively. This approach is based on the integration of these pre-trained models with the UNet and having an efficient network architecture. By transfer learning, these CNNs are fine-tuned to segment Breast Ultrasound images in normal and tumoral pixels. An extensive experiment of our proposed architecture has been done using Breast Ultrasound Dataset B. Quantitative metrics for evalu?ation of segmentation results including Dice coefficient, Precision, Recall, and , all reached over 80% , which indicates that the method proposed has the capacity to distinguish functional tissues in breast ultrasound images. Thus, our proposed method might have the potential to pro?vide the segmentations necessary to assist the clinical diagnosis of breast cancer and improve imaging in other modes in medical ultrasound.

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Deep integrated pipeline of segmentation guided classification of breast cancer from ultrasound images

Cited by 43 publications

References 40 publications

An intelligent deep convolutional network based COVID-19 detection from chest X-rays

An intelligent deep convolutional network based COVID-19 detection from chest X-rays

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

Hybrid UNET Model Segmentation for an Early Breast Cancer Detection using Ulrasound Images

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