Breast cancer classification using deep learned features boosted with handcrafted features

Sajid, Unaiza; Khan, Rizwan Ahmed; Shah, Shahid Munir; Arif, Sheeraz

doi:10.1016/j.bspc.2023.105353

Cited by 22 publications

(3 citation statements)

References 66 publications

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“…These systems are, however, semi-automated since they require lesion GT masks or hard-cropped ROIs from qualified radiologists to generate several types of segmentation-enhanced input images. (Sajid et al 2023). Although these hybrid methods show improved classification performance by incorporating domain expert knowledge from handcrafted features, a direct combination strategy necessitates optimum feature selection from all possible feature sets.…”

Section: Deep-learning Based Methods For Breast Tumor Classificationmentioning

confidence: 99%

CAM-QUS guided self-tuning modular CNNs with multi-loss functions for fully automated breast lesion classification in ultrasound images

Tasnim,

Hasan

2023

Phys. Med. Biol.

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Objective. Breast cancer is the major cause of cancer death among women worldwide. Deep learning-based computer-aided diagnosis (CAD) systems for classifying lesions in breast ultrasound images can help materialise the early detection of breast cancer and enhance survival chances. Approach. This paper presents a completely automated BUS diagnosis system with modular convolutional neural networks tuned with novel loss functions. The proposed network comprises a dynamic channel input enhancement network, an attention-guided InceptionV3-based feature extraction network, a classification network, and a parallel feature transformation network to map deep features into quantitative ultrasound (QUS) feature space. These networks function together to improve classification accuracy by increasing the separation of benign and malignant class-specific features and enriching them simultaneously. Unlike the categorical crossentropy (CCE) loss-based traditional approaches, our method uses two additional novel losses: class activation mapping (CAM)-based and QUS feature-based losses, to capacitate the overall network learn the extraction of clinically valued lesion shape and texture-related properties focusing primarily the lesion area for explainable AI (XAI). Main results. Experiments on four public, one private, and a combined breast ultrasound dataset are used to validate our strategy. The suggested technique obtains an accuracy of 97.28%, sensitivity of 93.87%, F1-score of 95.42% on dataset 1 (BUSI), and an accuracy of 91.50%, sensitivity of 89.38%, and F1-score of 89.31% on the combined dataset, consisting of 1494 images collected from hospitals in five demographic locations using four ultrasound systems of different manufacturers. These results outperform techniques reported in the literature by a considerable margin. Significance. The proposed CAD system provides diagnosis from the auto-focused lesion area of B-mode BUS images, avoiding the explicit requirement of any segmentation or region of interest extraction, and thus can be a handy tool for making accurate and reliable diagnoses even in unspecialized healthcare centers.

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Section: Deep-learning Based Methods For Breast Tumor Classificationmentioning

confidence: 99%

CAM-QUS guided self-tuning modular CNNs with multi-loss functions for fully automated breast lesion classification in ultrasound images

Tasnim,

Hasan

2023

Phys. Med. Biol.

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“…The shape pattern characteristics are calculated based on the gradient and edge pixel’s orientation. In terms of breast tumor's shape feature analysis, the HOG is particularly valuable as it can emphasize the mass structures (Sajid et al 2023 ). HOG divides the images into smaller portion and calculates the gradient along with its orientation for each section.…”

Section: Methodsmentioning

confidence: 99%

Graph neural network-based breast cancer diagnosis using ultrasound images with optimized graph construction integrating the medically significant features

Chowa,

Azam,

Montaha

et al. 2023

J Cancer Res Clin Oncol

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Purpose An automated computerized approach can aid radiologists in the early diagnosis of breast cancer. In this study, a novel method is proposed for classifying breast tumors into benign and malignant, based on the ultrasound images through a Graph Neural Network (GNN) model utilizing clinically significant features. Method Ten informative features are extracted from the region of interest (ROI), based on the radiologists’ diagnosis markers. The significance of the features is evaluated using density plot and T test statistical analysis method. A feature table is generated where each row represents individual image, considered as node, and the edges between the nodes are denoted by calculating the Spearman correlation coefficient. A graph dataset is generated and fed into the GNN model. The model is configured through ablation study and Bayesian optimization. The optimized model is then evaluated with different correlation thresholds for getting the highest performance with a shallow graph. The performance consistency is validated with k-fold cross validation. The impact of utilizing ROIs and handcrafted features for breast tumor classification is evaluated by comparing the model’s performance with Histogram of Oriented Gradients (HOG) descriptor features from the entire ultrasound image. Lastly, a clustering-based analysis is performed to generate a new filtered graph, considering weak and strong relationships of the nodes, based on the similarities. Results The results indicate that with a threshold value of 0.95, the GNN model achieves the highest test accuracy of 99.48%, precision and recall of 100%, and F1 score of 99.28%, reducing the number of edges by 85.5%. The GNN model’s performance is 86.91%, considering no threshold value for the graph generated from HOG descriptor features. Different threshold values for the Spearman’s correlation score are experimented with and the performance is compared. No significant differences are observed between the previous graph and the filtered graph. Conclusion The proposed approach might aid the radiologists in effective diagnosing and learning tumor pattern of breast cancer.

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“…Molecular subtyping is closely related to devising clinical strategy and prognosis, and deep learning-based models have been shown to accurately classify these subtypes [45]. In contrast to cutting-edge outcomes, deep learning in conjunction with other techniques like HOG and LBP outperforms them when it comes to mammogram-based breast cancer classification [46]. Deep learning has also been used to study the connection between breast cancer subtypes and genes.…”

Section: Importance and Relevance Deep Learning In Breast Cancer Clas...mentioning

confidence: 99%

Breast Cancer Classification Utilizing Deep Learning Techniques on Medical Images: A Comprehensive Review

Marwah K. Muttair

2024

jes

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Breast cancer is a major public health concern, affecting millions of women worldwide. Good patient outcomes and a successful course of therapy depend on a prompt and accurate diagnosis. The application of deep learning algorithms to the breast cancer classification problem has yielded some encouraging results; this might open the way for more rapid and accurate diagnostics. This article examines the many deep learning (DL) approaches taken to date for BC classification tasks, outlining each one's strengths, weaknesses, and current challenges. This study discusses several DL algorithms like convolutional neural networks, multi-layer neural networks, and autoencoders that analyze histopathological pictures, mammograms, and other forms of medical imaging. Furthermore, we investigate the interpretability & explainability features of DL models as they pertain to BC diagnoses, drawing attention to the need for reliable decision-making tools for medical practitioners. Throughout the review, we identify challenges and potential biases in current research. Finally, we provide an outlook on the future directions of DL in BC classification, focusing on promising research areas. By highlighting the achievements and gaps in the existing literature, this review aims to inspire further advancements in DL-based BC diagnosis, eventually resulting in better healthcare and reliable diagnosis.

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Breast cancer classification using deep learned features boosted with handcrafted features

Cited by 22 publications

References 66 publications

CAM-QUS guided self-tuning modular CNNs with multi-loss functions for fully automated breast lesion classification in ultrasound images

CAM-QUS guided self-tuning modular CNNs with multi-loss functions for fully automated breast lesion classification in ultrasound images

Graph neural network-based breast cancer diagnosis using ultrasound images with optimized graph construction integrating the medically significant features

Breast Cancer Classification Utilizing Deep Learning Techniques on Medical Images: A Comprehensive Review

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