Preprocessing of Breast Cancer Images to Create Datasets for Deep-CNN

Beeravolu, Abhijith Reddy; Azam, Sami; Jonkman, Mirjam; Shanmugam, Bharanidharan; Kannoorpatti, Krishnan; Anwar, Adnan

doi:10.1109/access.2021.3058773

Cited by 85 publications

(53 citation statements)

References 54 publications

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“…Therefore, we first focus on image pre-processing. Beeravolu et al [ 9 ] describes diverse pre-processing techniques which can be implemented without affecting the original image quality. Two other studies [ 4 , 24 ] used well-known methods to enhance the contrast of mammograms.…”

Section: Image Preprocessing Techniquesmentioning

confidence: 99%

“…Thirdly, Image enhancement, a procedure of improving the brightness and contrast of original images, is applied to make the cancerous lesion more visible. The sub processes of this step are gamma correction [ 41 ], CLAHE(1st) [ 42 ], CLAHE(2nd) and a filter of ImageJ software: green fire blue [ 9 ]. After applying CLAHE, an improvement of visibility can be noticed.…”

Section: Image Preprocessing Techniquesmentioning

confidence: 99%

“…The final enhancement process uses the ImageJ software tool called “Look Up Tables (LUTs)” [ 9 ]. This filter is applied on the image to separate the tumor, dense tissues, and surrounding cells in three different colors.…”

Section: Image Preprocessing Techniquesmentioning

confidence: 99%

“…This is compounded by the fact that there is a lack of radiologists and doctors across the world who can interpret the screening results, especially in regional areas and developing countries [ 8 ]. As time is a major factor in saving lives in the case of breast cancer, human resources and technology to deliver prompt patient services in terms of screening, diagnosis, and treatment are essential [ 9 ]. However, tumor diagnosis is time-consuming and often challenging for radiologists, by examining medical images due to the presence of noise, artefacts, and complex structure.…”

Section: Introductionmentioning

confidence: 99%

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BreastNet18: A High Accuracy Fine-Tuned VGG16 Model Evaluated Using Ablation Study for Diagnosing Breast Cancer from Enhanced Mammography Images

Montaha

Azam

Rafid

et al. 2021

Biology

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Background: Identification and treatment of breast cancer at an early stage can reduce mortality. Currently, mammography is the most widely used effective imaging technique in breast cancer detection. However, an erroneous mammogram based interpretation may result in false diagnosis rate, as distinguishing cancerous masses from adjacent tissue is often complex and error-prone. Methods: Six pre-trained and fine-tuned deep CNN architectures: VGG16, VGG19, MobileNetV2, ResNet50, DenseNet201, and InceptionV3 are evaluated to determine which model yields the best performance. We propose a BreastNet18 model using VGG16 as foundational base, since VGG16 performs with the highest accuracy. An ablation study is performed on BreastNet18, to evaluate its robustness and achieve the highest possible accuracy. Various image processing techniques with suitable parameter values are employed to remove artefacts and increase the image quality. A total dataset of 1442 preprocessed mammograms was augmented using seven augmentation techniques, resulting in a dataset of 11,536 images. To investigate possible overfitting issues, a k-fold cross validation is carried out. The model was then tested on noisy mammograms to evaluate its robustness. Results were compared with previous studies. Results: Proposed BreastNet18 model performed best with a training accuracy of 96.72%, a validating accuracy of 97.91%, and a test accuracy of 98.02%. In contrast to this, VGGNet19 yielded test accuracy of 96.24%, MobileNetV2 77.84%, ResNet50 79.98%, DenseNet201 86.92%, and InceptionV3 76.87%. Conclusions: Our proposed approach based on image processing, transfer learning, fine-tuning, and ablation study has demonstrated a high correct breast cancer classification while dealing with a limited number of complex medical images.

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Section: Image Preprocessing Techniquesmentioning

confidence: 99%

Section: Image Preprocessing Techniquesmentioning

confidence: 99%

Section: Image Preprocessing Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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BreastNet18: A High Accuracy Fine-Tuned VGG16 Model Evaluated Using Ablation Study for Diagnosing Breast Cancer from Enhanced Mammography Images

Montaha

Azam

Rafid

et al. 2021

Biology

Self Cite

View full text Add to dashboard Cite

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“…CNN-based FDD has the following advantages: (i) Industrial system data has multi-source heterogeneity [41]- [44]. The input of CNN can be time series [45]- [47], spectrogram [48], [49], and images [50]- [52], which is suitable for multisource information processing [41], [53]; (ii) Complex PV systems are often accompanied by random strong magnetic interference, high temperatures. The features extracted by CNN have translation invariance [54], [55], which increases the robustness of the diagnosis algorithm and improves the generalization ability of CNN; (iii) The data that can characterize the faults in PV systems is often submerged in massive real-time data.…”

Section: A Convolutional Neural Network Based Fault Diagnosismentioning

confidence: 99%

Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects

Mansouri¹,

Trabelsi

Nounou

et al. 2021

IEEE Access

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Photovoltaic (PV) systems are subject to failures during their operation due to the aging effects and external/environmental conditions. These faults may affect the different system components such as PV modules, connection lines, converters/inverters, which can lead to a decrease in the efficiency, performance, and further system collapse. Thus, a key factor to be taken into consideration in high-efficiency grid-connected PV systems is the fault detection and diagnosis (FDD). The performance of the FDD method depends mainly on the quality of the extracted features including real-time changes, phase changes, trend changes, and faulty modes. Thus, the data representation learning is the core stage of intelligent FDD techniques. Recently, due to the enhancement of computing capabilities, the increase of the big data use, and the development of effective algorithms, the deep learning (DL) tool has witnessed a great success in data science. Therefore, this paper proposes an extensive review on deep learning based FDD methods for PV systems. After a brief description of the DL-based strategies, techniques for diagnosing PV systems proposed in recent literature are overviewed and analyzed to point out their differences, advantages and limits. Future research directions towards the improvement of the performance of the DL-based FDD techniques are also discussed. This review paper aims to systematically present the development of DLbased FDD for PV systems and provide guidelines for future research in the field.

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Feature Density as an Uncertainty Estimator Method in the Binary Classification Mammography Images Task for a Supervised Deep Learning Model

Fuentes-Fino,

Calderón-Ramírez,

Domínguez

et al. 2022

Bioinformatics and Biomedical Engineering

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Labeled medical datasets may include a limited number of observations for each class, while unlabeled datasets may include observations from patients with pathologies other than those observed in the labeled dataset. This negatively influences the performance of the prediction algorithms. Including out-of-distribution data in the unlabeled dataset can lead to varying degrees of performance degradation, or even improvement, by using a distance to measure how out-of-distribution a piece of data is. This work aims to propose an approach that allows estimating the predictive uncertainty of supervised algorithms, improving the behaviour when atypical samples are presented to the distribution of the dataset. In particular, we have used this approach to mammograms X-ray images applied to binary classification tasks. The proposal makes use of Feature Density, which consists of estimating the density of features from the calculation of a histogram. The obtained results report slight differences when different neural network architectures and uncertainty estimators are used.

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Preprocessing of Breast Cancer Images to Create Datasets for Deep-CNN

Cited by 85 publications

References 54 publications

BreastNet18: A High Accuracy Fine-Tuned VGG16 Model Evaluated Using Ablation Study for Diagnosing Breast Cancer from Enhanced Mammography Images

BreastNet18: A High Accuracy Fine-Tuned VGG16 Model Evaluated Using Ablation Study for Diagnosing Breast Cancer from Enhanced Mammography Images

Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects

Feature Density as an Uncertainty Estimator Method in the Binary Classification Mammography Images Task for a Supervised Deep Learning Model

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