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

Biology ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1347
Author(s):  
Sidratul Montaha ◽  
Sami Azam ◽  
Abul Kalam Muhammad Rakibul Haque Rafid ◽  
Pronab Ghosh ◽  
Md. Zahid Hasan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Early Stage ◽  
Fine Tuning ◽  
Test Accuracy ◽  
Suitable Parameter ◽  
Augmentation Techniques ◽  
Diagnosis Rate ◽  
Ablation Study ◽  
Parameter Values

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.

AUTOMATIC SEGMENTATION OF BREAST TISSUE THERMAL IMAGES

2018 ◽  
Vol 30 (03) ◽  
pp. 1850024 ◽  
Author(s):  
Zeinab Heidari ◽  
Mehrdad Dadgostar ◽  
Zahra Einalou
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Breast Imaging ◽  
Breast Tissue ◽  
Early Stage ◽  
Automatic Segmentation ◽  
Image Data ◽  
Thermal Images ◽  
Early Stages ◽  
Invasive Method

Breast cancer is one of the main causes of women’s death. Thermal breast imaging is one the non-invasive method for cancer at early stage diagnosis. In contrast to mammography this method is cheap and painless and it can be used during pregnancy while ionized beams are not used. Specialists are seeking new ways to diagnose the cancer in early stages. Segmentation of the breast tissue is one of the most indispensable stages in most of the cancer diagnosis methods. By the advancement of infrared precise cameras, new and fast computers and nouvelle image processing approaches, it is feasible to use thermal imaging for diagnosis of breast cancer at early stages. Since the breast form is different in individuals, image segmentation is a hard task and semi-automatic or manual methods are usual in investigations. In this research the image data base of DMR-IR has been utilized and a now automatic approach has been proposed which does not need learning. Data were included 159 gray images used by dynamic protocol (132 healthy and 27 patients). In this study, by combination of different image processing methods, the segmentation of thermal images of the breast tissues have been completed automatically and results show the proper performance of recommended method.

scholarly journals A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Lian Zou ◽  
Shaode Yu ◽  
Tiebao Meng ◽  
Zhicheng Zhang ◽  
Xiaokun Liang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Cancer Diagnosis ◽  
Early Stage ◽  
Breast Cancer Diagnosis ◽  
Fine Tuning ◽  
Learning Tools ◽  
Pros And Cons ◽  
Future Work

This study reviews the technique of convolutional neural network (CNN) applied in a specific field of mammographic breast cancer diagnosis (MBCD). It aims to provide several clues on how to use CNN for related tasks. MBCD is a long-standing problem, and massive computer-aided diagnosis models have been proposed. The models of CNN-based MBCD can be broadly categorized into three groups. One is to design shallow or to modify existing models to decrease the time cost as well as the number of instances for training; another is to make the best use of a pretrained CNN by transfer learning and fine-tuning; the third is to take advantage of CNN models for feature extraction, and the differentiation of malignant lesions from benign ones is fulfilled by using machine learning classifiers. This study enrolls peer-reviewed journal publications and presents technical details and pros and cons of each model. Furthermore, the findings, challenges and limitations are summarized and some clues on the future work are also given. Conclusively, CNN-based MBCD is at its early stage, and there is still a long way ahead in achieving the ultimate goal of using deep learning tools to facilitate clinical practice. This review benefits scientific researchers, industrial engineers, and those who are devoted to intelligent cancer diagnosis.

scholarly journals Artificial Intelligence for early Detection of Breast Cancer and Classification of Mammographic Masses

2020 ◽  
Vol 8 (6) ◽  
pp. 4314-4320
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Image Processing ◽  
Early Stage ◽  
Probabilistic Neural Network ◽  
Breast Lesions ◽  
Test Image ◽  
False Positive Detection ◽  
Processing Techniques

Every single year thousands of women endure painful and invasive surgery to remove breast lesions. Most of the time the mammographic image analysis leads to false positive detection and the majority of this actions reveal the lesions to be benign. Refining present detection and diagnostic tool is a major priority of our work. MATLAB R2015a is been used to develop the algorithm, which aids in detection of breast cancer in its early stage. The algorithm comprises of image processing and applying artificial intelligence where in the system is trained with a set of images so that when the input or the test image is given, the algorithm performs the image processing techniques and then applies the Probabilistic Neural Network (PNN) technique for detection of cancer. The system performance is also been calculated in order to estimate its reliability.

Advancement in Research Techniques on Medical Imaging Processing for Breast Cancer Detection

2016 ◽  
Vol 6 (2) ◽  
pp. 717
Author(s):  
Sushma S J ◽  
S C Prasanna Kumar
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Medical Image ◽  
Early Stage ◽  
Medical Image Processing ◽  
Healthcare Sector ◽  
Diagnostic Tools ◽  
Success Factor ◽  
Research Activity ◽  
Effective Diagnosis

With the advancement of medical image processing, the area of the healthcare sector has started receiving the benefits of the modern arena of diagnostic tools to identify the diseases effectively. Cancer is one of the dreaded diseases, where success factor of treatment offered by medical sector is still an unsolved problem. Hence, the success factor of the treatment lies in early stage of the disease or timely detection of the disease. This paper discusses about the advancement being made in the medical image processing towards an effective diagnosis of the breast cancer from the mammogram image in radiology. There has been enough research activity with various sorts of advances techniques being implemented in the past decade. The prime contribution of this manuscript is to showcase the advancement of the technology along with illustration of the effectiveness of the existing literatures with respect to research gap.

scholarly journals Advancement in Research Techniques on Medical Imaging Processing for Breast Cancer Detection

2016 ◽  
Vol 6 (2) ◽  
pp. 717
Author(s):  
Sushma S J ◽  
S C Prasanna Kumar
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Medical Image ◽  
Early Stage ◽  
Medical Image Processing ◽  
Healthcare Sector ◽  
Diagnostic Tools ◽  
Success Factor ◽  
Research Activity ◽  
Effective Diagnosis

With the advancement of medical image processing, the area of the healthcare sector has started receiving the benefits of the modern arena of diagnostic tools to identify the diseases effectively. Cancer is one of the dreaded diseases, where success factor of treatment offered by medical sector is still an unsolved problem. Hence, the success factor of the treatment lies in early stage of the disease or timely detection of the disease. This paper discusses about the advancement being made in the medical image processing towards an effective diagnosis of the breast cancer from the mammogram image in radiology. There has been enough research activity with various sorts of advances techniques being implemented in the past decade. The prime contribution of this manuscript is to showcase the advancement of the technology along with illustration of the effectiveness of the existing literatures with respect to research gap.

Thermal Imaging Techniques for Breast Screening - A Survey

2020 ◽  
Vol 16 (7) ◽  
pp. 855-862
Author(s):  
Prabha S.
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Early Stage ◽  
Imaging Techniques ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Thermal Images ◽  
Low Contrast ◽  
Noise Ratio ◽  
Breast Tissues

Breast cancer is the second leading cause of cancer death among women preceded by cervix cancer. It has been reported that at the early stage of detection there is 85% chance of getting cured, whereas only 10% chance at later stage diagnosis. The current screening modalities are expensive, they have intricate imaging measures and they are unhealthy due to radiation exposure. Therefore, a screening tool that is non-invasive, has no connection with the body, free from radiation, such as Medical Thermography is necessary. It is reported that the sensitivity and specificity of medical thermography are high largely in dense breast tissues. The clinical interpretation primarily depends on the asymmetrical analysis of these thermograms subjectively. The appearance of an asymmetric thermal image may indicate the pathological conditions. For earlier detection of breast cancer, it is essential to identify the advanced methods in image processing techniques which enhance the significance of diagnostics. In that analysis, the required breast region is unglued from the background image. The segmented image is separated into symmetrical left and right breast tissues. The statistical and histogram features extracted from both regions are used to identify the abnormal thermograms using machine learning algorithms. From literature, it is reported that the thermal images are inherently low contrast images and have low single to noise ratio. Moreover, they are amorphous in nature and no clear edges are seen. The difficulty lies in the detection of lower breast boundaries and inframammary folds. So, in general, the first attempt is made in improving the signal to noise ratio and contrast of the image which helps to extract the true regions of breast tissues. Then, asymmetry analysis of the normal and abnormal breast tissues is performed using different techniques. This work demonstrates the review of a few image processing methods or the development which are elaborated in the detection of breast cancer from thermal images.

scholarly journals A hybrid ensemble deep learning approach for reliable breast cancer detection

2021 ◽  
Vol 7 (2) ◽  
pp. 112
Author(s):  
Mohamed Abdelmoneim Elshafey ◽  
Tarek Elsaid Ghoniemy
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Deep Learning ◽  
Regression Model ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Fine Tuning ◽  
Classification Model ◽  
Support Vector ◽  
Augmentation Techniques

Among the cancer diseases, breast cancer is considered one of the most prevalent threats requiring early detection for a higher recovery rate. Meanwhile, the manual evaluation of malignant tissue regions in histopathology images is a critical and challenging task. Nowadays, deep learning becomes a leading technology for automatic tumor feature extraction and classification as malignant or benign. This paper presents a proposed hybrid deep learning-based approach, for reliable breast cancer detection, in three consecutive stages: 1) fine-tuning the pre-trained Xception-based classification model, 2) merging the extracted features with the predictions of a two-layer stacked LSTM-based regression model, and finally, 3) applying the support vector machine, in the classification phase, to the merged features. For the three stages of the proposed approach, training and testing phases are performed on the BreakHis dataset with nine adopted different augmentation techniques to ensure generalization of the proposed approach. A comprehensive performance evaluation of the proposed approach, with diverse metrics, shows that employing the LSTM-based regression model improves accuracy and precision metrics of the fine-tuned Xception-based model by 10.65% and 11.6%, respectively. Additionally, as a classifier, implementing the support vector machine further boosts the model by 3.43% and 5.22% for both metrics, respectively. Experimental results exploit the efficiency of the proposed approach with outstanding reliability in comparison with the recent state-of-the-art approaches.

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