Deep transfer learning for IDC breast cancer detection using fast AI technique and Sqeezenet architecture

Chaudhury, Sushovan; Sau, Kartik; Khan, Muhammad Attique; Shabaz, Mohammad

doi:10.3934/mbe.2023457

Cited by 9 publications

(5 citation statements)

References 39 publications

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“…Faimed3D is a related version of FastAI [15], an open-source library for 2D CNN. The feasibility of the FastAI framework for medical images has been investigated in previous studies [17,18]. To date, no studies have examined the utility of Faimed3D-based classi cation.…”

Section: Discussionmentioning

confidence: 99%

Utility of a Three-dimensional Convolutional Neural Network-based Renal Function Prediction Using Kidney MRI in Chronic Kidney Disease

Nagawa,

Hara,

Inoue

et al. 2024

Preprint

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A three-dimensional convolutional neural network model was developed to predict renal function in patients with chronic kidney disease (CKD) using magnetic resonance imaging (MRI) Dixon-based T1-weighted in-phase (IP)/opposed-phase (OP)/water-only (WO) images. Seventy-three patients with severe renal dysfunction (estimated glomerular filtration rate [eGFR] <30 mL/min/1.73 m2, CKD stage G4–5); 172 with moderate renal dysfunction (30≤ eGFR <60 mL/min/1.73 m2, CKD stage G3a/b); and 76 in the control (eGFR ≥60 mL/min/1.73 m2, CKD stage G1–2) groups participated in this study. The model was applied to the right, left, and both kidneys, as well as for each imaging method (T1-weighted IP/OP/WO images). The best performance was obtained when using bilateral kidneys and IP images, with an accuracy of 0.862 ± 0.036. The overall accuracy was better for bilateral kidney models than that for unilateral kidney models. Our deep-learning approach using kidney MRI could apply to the evaluation of renal function in patients with CKD.

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Section: Discussionmentioning

confidence: 99%

Utility of a Three-dimensional Convolutional Neural Network-based Renal Function Prediction Using Kidney MRI in Chronic Kidney Disease

Nagawa,

Hara,

Inoue

et al. 2024

Preprint

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“…Several techniques for contrast enhancement have been developed in the literature, including hybrid filters, color transformations, and haze reduction with a dark channel ( 22 ). This step’s improved performance improved the accuracy of lesion segmentation, which has an impact on useful feature extraction ( 23 ). Several lesion segmentation techniques, such as thresholding, saliency and region growing, and clustering, have been developed in the literature.…”

Section: Introductionmentioning

confidence: 99%

A novel framework of multiclass skin lesion recognition from dermoscopic images using deep learning and explainable AI

et al. 2023

Self Cite

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Skin cancer is a serious disease that affects people all over the world. Melanoma is an aggressive form of skin cancer, and early detection can significantly reduce human mortality. In the United States, approximately 97,610 new cases of melanoma will be diagnosed in 2023. However, challenges such as lesion irregularities, low-contrast lesions, intraclass color similarity, redundant features, and imbalanced datasets make improved recognition accuracy using computerized techniques extremely difficult. This work presented a new framework for skin lesion recognition using data augmentation, deep learning, and explainable artificial intelligence. In the proposed framework, data augmentation is performed at the initial step to increase the dataset size, and then two pretrained deep learning models are employed. Both models have been fine-tuned and trained using deep transfer learning. Both models (Xception and ShuffleNet) utilize the global average pooling layer for deep feature extraction. The analysis of this step shows that some important information is missing; therefore, we performed the fusion. After the fusion process, the computational time was increased; therefore, we developed an improved Butterfly Optimization Algorithm. Using this algorithm, only the best features are selected and classified using machine learning classifiers. In addition, a GradCAM-based visualization is performed to analyze the important region in the image. Two publicly available datasets—ISIC2018 and HAM10000—have been utilized and obtained improved accuracy of 99.3% and 91.5%, respectively. Comparing the proposed framework accuracy with state-of-the-art methods reveals improved and less computational time.

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“…However, the increase in computational time due to the feature fusion step may be a consideration for practical implementation. Chaudhury et al [29] presented a novel approach to texture classification in invasive ductal carcinoma (IDC) using transfer learning with super convergence. The study introduces a lightweight model named Squeeze Net [30] for easy deployment on mobile devices and incorporates data augmentation and color normalization techniques to improve performance.…”

Section: Introductionmentioning

confidence: 99%

Enhancing breast ultrasound segmentation through fine-tuning and optimization techniques: Sharp attention UNet

Khaledyan,

Marini,

M. Baran

et al. 2023

PLoS ONE

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Segmentation of breast ultrasound images is a crucial and challenging task in computer-aided diagnosis systems. Accurately segmenting masses in benign and malignant cases and identifying regions with no mass is a primary objective in breast ultrasound image segmentation. Deep learning (DL) has emerged as a powerful tool in medical image segmentation, revolutionizing how medical professionals analyze and interpret complex imaging data. The UNet architecture is a highly regarded and widely used DL model in medical image segmentation. Its distinctive architectural design and exceptional performance have made it popular among researchers. With the increase in data and model complexity, optimization and fine-tuning models play a vital and more challenging role than before. This paper presents a comparative study evaluating the effect of image preprocessing and different optimization techniques and the importance of fine-tuning different UNet segmentation models for breast ultrasound images. Optimization and fine-tuning techniques have been applied to enhance the performance of UNet, Sharp UNet, and Attention UNet. Building upon this progress, we designed a novel approach by combining Sharp UNet and Attention UNet, known as Sharp Attention UNet. Our analysis yielded the following quantitative evaluation metrics for the Sharp Attention UNet: the Dice coefficient, specificity, sensitivity, and F1 score values obtained were 0.93, 0.99, 0.94, and 0.94, respectively. In addition, McNemar’s statistical test was applied to assess significant differences between the approaches. Across a number of measures, our proposed model outperformed all other models, resulting in improved breast lesion segmentation.

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Deep transfer learning for IDC breast cancer detection using fast AI technique and Sqeezenet architecture

Cited by 9 publications

References 39 publications

Utility of a Three-dimensional Convolutional Neural Network-based Renal Function Prediction Using Kidney MRI in Chronic Kidney Disease

Utility of a Three-dimensional Convolutional Neural Network-based Renal Function Prediction Using Kidney MRI in Chronic Kidney Disease

A novel framework of multiclass skin lesion recognition from dermoscopic images using deep learning and explainable AI

Enhancing breast ultrasound segmentation through fine-tuning and optimization techniques: Sharp attention UNet

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