Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network

Busaleh, Mariam; Hussain, Muhammad; Aboalsamh, Hatim; Amin, Fazal

doi:10.3390/bios11110419

Cited by 11 publications

(5 citation statements)

References 49 publications

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“…In this study, a robust shallow CNN model that can perform with optimal accuracy for all eight datasets even with the same parameters is developed. We consider that the most efficient way to achieve this is to develop the architecture using the mammogram dataset, which is regarded as one of the most challenging imaging modalities ( 86 ). For this goal, a number of ablation studies were conducted to generate the proposed MNet-10 model.…”

Section: Discussionmentioning

confidence: 99%

MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique

et al. 2022

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Interpretation of medical images with a computer-aided diagnosis (CAD) system is arduous because of the complex structure of cancerous lesions in different imaging modalities, high degree of resemblance between inter-classes, presence of dissimilar characteristics in intra-classes, scarcity of medical data, and presence of artifacts and noises. In this study, these challenges are addressed by developing a shallow convolutional neural network (CNN) model with optimal configuration performing ablation study by altering layer structure and hyper-parameters and utilizing a suitable augmentation technique. Eight medical datasets with different modalities are investigated where the proposed model, named MNet-10, with low computational complexity is able to yield optimal performance across all datasets. The impact of photometric and geometric augmentation techniques on different datasets is also evaluated. We selected the mammogram dataset to proceed with the ablation study for being one of the most challenging imaging modalities. Before generating the model, the dataset is augmented using the two approaches. A base CNN model is constructed first and applied to both the augmented and non-augmented mammogram datasets where the highest accuracy is obtained with the photometric dataset. Therefore, the architecture and hyper-parameters of the model are determined by performing an ablation study on the base model using the mammogram photometric dataset. Afterward, the robustness of the network and the impact of different augmentation techniques are assessed by training the model with the rest of the seven datasets. We obtain a test accuracy of 97.34% on the mammogram, 98.43% on the skin cancer, 99.54% on the brain tumor magnetic resonance imaging (MRI), 97.29% on the COVID chest X-ray, 96.31% on the tympanic membrane, 99.82% on the chest computed tomography (CT) scan, and 98.75% on the breast cancer ultrasound datasets by photometric augmentation and 96.76% on the breast cancer microscopic biopsy dataset by geometric augmentation. Moreover, some elastic deformation augmentation methods are explored with the proposed model using all the datasets to evaluate their effectiveness. Finally, VGG16, InceptionV3, and ResNet50 were trained on the best-performing augmented datasets, and their performance consistency was compared with that of the MNet-10 model. The findings may aid future researchers in medical data analysis involving ablation studies and augmentation techniques.

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Section: Discussionmentioning

confidence: 99%

MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique

et al. 2022

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“…Many recent studies have explored the potential of convolutional neural networks (CNNs) for classifying extensive datasets [8,9,16,17]. For instance, the research study in [8] transformed gene expression datasets from 11 cancer types into 2D images using spectral clustering and achieved a classification accuracy ranging between 97.7% and 98.4% using CNNs.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, the research study in [8] transformed gene expression datasets from 11 cancer types into 2D images using spectral clustering and achieved a classification accuracy ranging between 97.7% and 98.4% using CNNs. Another work, [16], utilized both support vector machines and CNNs to detect early breast cancer signs, outperforming existing classification methods for benign and malignant mass regions. This approach could aid radiologists in improving their diagnostic accuracy.…”

Section: Related Workmentioning

confidence: 99%

Applying a Recurrent Neural Network-Based Deep Learning Model for Gene Expression Data Classification

Babichev,

Liakh,

Kalinina

2023

Applied Sciences

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The importance of gene expression data processing in solving the classification task is determined by its ability to discern intricate patterns and relationships within genetic information, enabling the precise categorization and understanding of various gene expression profiles and their consequential impacts on biological processes and traits. In this study, we investigated various architectures and types of recurrent neural networks focusing on gene expression data. The effectiveness of the appropriate model was evaluated using various classification quality criteria based on type 1 and type 2 errors. Moreover, we calculated the integrated F1-score index using the Harrington desirability method, the value of which allowed us to improve the objectivity of the decision making when model effectiveness was evaluated. The final decision regarding model effectiveness was made based on a comprehensive classification quality criterion, which was calculated as the weighted sum of classification accuracy, integrated F1-score index, and loss function values. The simulation results show higher appeal of a single-layer GRU recurrent network with 75 neurons in the recurrent layer. We also compared convolutional and recurrent neural networks on gene expression data classification. Although convolutional neural networks showcase benefits in terms of loss function value and training time, a comparative analysis revealed that in terms of classification accuracy calculated on the test data subset, the GRU neural network model is slightly better than the CNN and LSTM models. The classification accuracy when using the GRU network was 97.2%; in other cases, it was 97.1%. In the first case, 954 out of 981 objects were correctly identified. In other cases, 952 objects were correctly identified.

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“…Despite technological advancements such as tomosynthesis, introduced to enhance breast cancer screening and early-stage diagnosis for more effective treatment, challenges persist in the frequent occurrence of false positive mammograms, variability among expert readers, patient anxiety, as well as financial and opportunity costs 2,3,4,5,6 . Given these obstacles, alongside efforts promoting screening access, significant endeavors have been made in developing software for computer-aided diagnosis (CAD) to interpret abnormal mammograms 7,8,9,10,11,12,13,14 . However, the effectiveness of various algorithms for CAD has been vigorously questioned due to challenges such as difficulties in replication for diverse reasons, a significant drop in performance, and constraints in training with limited datasets 15,16,17,18,19 .…”

Section: Introductionmentioning

confidence: 99%

Transparency in Computer-Aided Breast Cancer Diagnosis Tool Development: A CBIS-DDSM Case Study

Liao,

Aagaard

2024

Preprint

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Accessible mammography datasets and innovative machine learning techniques are at the front line for computer-aided breast cancer diagnosis. However, the opacity surrounding private datasets and the unclear methodology behind the selection of subset images from publicly available databases for model training and testing, coupled with the arbitrary incompleteness or inaccessibility of code, markedly intensifies the obstacles in replicating and validating the model's efficacy. These challenges, in turn, erect barriers for subsequent researchers striving to learn and advance in this field. To address these limitations, we provide a pilot codebase covering the entire image preprocessing to model construction and evaluation pipeline, utilizing publicly available Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) mass subset, including both full images and regions of interests (ROIs). The pipeline achieves an area under the receiver operating characteristic curve (AUROC) of 86.3% [95% CI: 0.842, 0.885], representing the state of the art performance for benign and malignant classification with selected mammography images. Collectively, our efforts hold promise in accelerating global software development for breast cancer diagnosis by leveraging our codebase and structure, while also integrating other advancements in the field.

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Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network

Cited by 11 publications

References 49 publications

MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique

MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique

Applying a Recurrent Neural Network-Based Deep Learning Model for Gene Expression Data Classification

Transparency in Computer-Aided Breast Cancer Diagnosis Tool Development: A CBIS-DDSM Case Study

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