Deep Learning RN-BCNN Model for Breast Cancer BI-RADS Classification

Siddeeq, Shahbaz; Li, Jiyun; Bhatti, Hafiz Muhammad Ali; Manzoor, Arslan; Malhi, Umar Subhan

doi:10.1145/3447587.3447620

Cited by 10 publications

(3 citation statements)

References 9 publications

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“…Conversely, the simple CNN classifier demonstrates the quickest execution time but the lowest accuracy. On the other hand, LR utilizing the VGG approach performs reasonably well with the highest cross-validation [35] accuracy of 84.4%. In contrast, the ResNet50 classifier with hyperparameters exhibits the longest execution time of 4.023 seconds, despite achieving a relatively higher accuracy of 85%.…”

Section: Evaluate the State-of-the-art Methodsmentioning

confidence: 97%

Transfer Driven Ensemble Learning Approach using ROI Pooling CNN For Enhanced Breast Cancer Diagnosis

et al. 2023

JMC

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Cancer is a major cause of death that is brought on by the body's abnormal cell proliferation, including breast cancer. It poses a significant threat to the safety and health of people globally. Several imaging methods, such as mammography, CT scans, MRI, ultrasound, and biopsies, can help detect breast cancer. A biopsy is commonly done in histopathology to examine an image and assist in diagnosing breast cancer. However, accurately identifying the appropriate Region of Interest (ROI) remains challenging due to the complex nature of pre-processing phases, feature extracting regions, segmenting process and other conventional machine learning phases. This reduces the system's efficiency and accuracy. In order to reduce the variance that exists among viewers, the aim of this work is to build superior deep-learning phases algorithms. This research introduces a classifier that can detect and classify images simultaneously, without any human involvement. It employs a transfer-driven ensemble learning approach, where the framework comprises two main phases: production and detection of pseudo-color images and segmentation based on ROI Pooling CNN, which then feeds its output to ensemble models such as Efficientnet, ResNet101, and VGG19. Before the feature extraction process, data augmentation is necessary, involving minor adjustments like random cropping, horizontal flipping, and color space augmentations. Implementing and simulating the proposed segmentation and classification algorithms for any decision-making framework suggested could decrease the frequency of incorrect diagnoses and enhance classification accuracy. This could aid pathologists in obtaining a second opinion and facilitate the early identification of diseases. With a prediction accuracy of 98.3%, the proposed method outperforms the individual pre-trained models, namely Efficientnet, ResNet101, VGG16, and VGG19, by 2.3%, 1.71%, 2.01%, and 1.47%, respectively.

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Section: Evaluate the State-of-the-art Methodsmentioning

confidence: 97%

Transfer Driven Ensemble Learning Approach using ROI Pooling CNN For Enhanced Breast Cancer Diagnosis

et al. 2023

JMC

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“…In their paper "Deep Learning RN-BCNN Model for Breast Cancer BI-RADS Classification" [8], the researchers used a combination of augmentation strategies to prevent overfitting and improve the accuracy of mammogram analysis, including rotation, scaling and displacement. The proposed system was evaluated on the MIAS dataset and achieved 88% accuracy using the pre-trained ResNet101 classification network and 70% accuracy using the Nasnet-Mobile network.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Approach with Xception and NasNet for Early Breast Cancer Detection

Benajiba,

Chrayah,

Al-Amrani

2024

IJACSA

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Breast cancer is the most common cancer in women, accounting for 12.5% of global cancer cases in 2020, and the leading cause of cancer deaths in women worldwide. Early detection is therefore crucial to reducing deaths, and recent studies suggest that deep learning techniques can detect breast cancer more accurately than experienced doctors. Experienced doctors can detect breast cancer with only 79% accuracy, while machine learning techniques can achieve up to 91% accuracy (and sometimes up to 97%). To improve breast cancer classification, we conducted a study using two deep learning models, Xception and NasNet, which we combined to achieve better results in distinguishing between malignant and benign tumours in digital databases and cell images obtained from mammograms. Our hybrid model showed good classification results, with an accuracy of over 96.2% and an AUC of 0.993 (99.3%) for mammography data. Remarkably, these results outperformed all other models we compared them with, Top of Form ResNet101 and VGG, which only achieved accuracies of 87%, 88% and 84.4% respectively. Our results were also the best in the field, surpassing the accuracy of other recent hybrid models such as MOD-RES + NasMobile with 89.50% accuracy and VGG 16 + LR with 92.60% accuracy. By achieving this high accuracy rate, our work can make a significant contribution to reducing breast cancer deaths worldwide by helping doctors to detect the disease early and begin treatment immediately.

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“…The experiment was acted upon on two datasets, yielding 91% and 84% accuracy rates, respectively. Finally, Siddeeq et al [ 29 ] presented a framework with a ResNet-based customized neural network that was applied to an unbalanced dataset using the data augmentation and pyramid of scales approaches. The results obtained from the mammograms in the INbreast dataset show improved performance when the training dataset is increased.…”

Section: Related Workmentioning

confidence: 99%

BC2NetRF: Breast Cancer Classification from Mammogram Images Using Enhanced Deep Learning Features and Equilibrium-Jaya Controlled Regula Falsi-Based Features Selection

et al. 2023

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One of the most frequent cancers in women is breast cancer, and in the year 2022, approximately 287,850 new cases have been diagnosed. From them, 43,250 women died from this cancer. An early diagnosis of this cancer can help to overcome the mortality rate. However, the manual diagnosis of this cancer using mammogram images is not an easy process and always requires an expert person. Several AI-based techniques have been suggested in the literature. However, still, they are facing several challenges, such as similarities between cancer and non-cancer regions, irrelevant feature extraction, and weak training models. In this work, we proposed a new automated computerized framework for breast cancer classification. The proposed framework improves the contrast using a novel enhancement technique called haze-reduced local-global. The enhanced images are later employed for the dataset augmentation. This step aimed at increasing the diversity of the dataset and improving the training capability of the selected deep learning model. After that, a pre-trained model named EfficientNet-b0 was employed and fine-tuned to add a few new layers. The fine-tuned model was trained separately on original and enhanced images using deep transfer learning concepts with static hyperparameters’ initialization. Deep features were extracted from the average pooling layer in the next step and fused using a new serial-based approach. The fused features were later optimized using a feature selection algorithm known as Equilibrium-Jaya controlled Regula Falsi. The Regula Falsi was employed as a termination function in this algorithm. The selected features were finally classified using several machine learning classifiers. The experimental process was conducted on two publicly available datasets—CBIS-DDSM and INbreast. For these datasets, the achieved average accuracy is 95.4% and 99.7%. A comparison with state-of-the-art (SOTA) technology shows that the obtained proposed framework improved the accuracy. Moreover, the confidence interval-based analysis shows consistent results of the proposed framework.

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Deep Learning RN-BCNN Model for Breast Cancer BI-RADS Classification

Cited by 10 publications

References 9 publications

Transfer Driven Ensemble Learning Approach using ROI Pooling CNN For Enhanced Breast Cancer Diagnosis

Transfer Driven Ensemble Learning Approach using ROI Pooling CNN For Enhanced Breast Cancer Diagnosis

A Hybrid Approach with Xception and NasNet for Early Breast Cancer Detection

BC2NetRF: Breast Cancer Classification from Mammogram Images Using Enhanced Deep Learning Features and Equilibrium-Jaya Controlled Regula Falsi-Based Features Selection

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