Adaptive Threshold-Based Tumor Detection Algorithm For Mammograms Images

Breast cancer is without doubt the leading cancer among women, and it is one of the most damaging illnesses to females that should be periodically checked. Early detection of breast cancer can reduce the mortality caused by this disease by 95%. However, studies mention that up to 25% of tumors are missed by radiologists. In this paper, a tumor detection algorithm in mammogram images is developed by relying on simple calculations that are based on adaptive thresholding and tumor area size. Low complexity calculations will ease the implementation of the algorithm in embedded systems and in real-time detection. The proposed algorithm is used to detect the circular type of tumor and it is developed with a graphical user interface to ease the process of selecting mammogram images and changing settings of threshold values and the size of tumor area. Experimental results show the ability of the algorithm to successfully detect and differentiate circular tumors from normal and fatty breast tissue

Hyperparameter-Optimized Machine Learning Techniques for Mammogram Classification

Computer technology has employed Machine Learning models in a variety of applications to improve performance. The hyperparameter of a machine learning model must be adapted to overcome learning limitations and increase its performance. In this research, the hyperparameters of machine learning classifiers are tuned to identify cases of benign or malignant breast abnormalities. An experimental investigation was conducted using the Wisconsin Diagnosis Breast Cancer (WDBC) Dataset. A fusion model, Bayesian Optimization Hyper Band-Naïve Bayes (BOHB-NB) is employed, which is combined with conventional classification approaches like Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM).  The proposed methods are compared to cutting-edge models like SVM, NB, LR, K-Nearest Neighbour (KNN), Random Forest, and Decision Tree using a wide range of parametric measures, such as Precision, Recall, Specificity, F-measure, Accuracy, True Positivity Rate (TPR), and False Positivity Rate (FPR). The results show that the proposed methods outperform the leading models

ANALISA GAMBAR X-RAY MAMMOGRAPHY DENGAN CONVOLUTION NEURAL NETWORK PADA DEEP LEARNING DENGAN ARSITEKTUR RESNET

Kanker adalah penyakit yang terjadi ketika sel-sel tubuh mengalami perubahan dan tumbuh secara tidak terkendali. Kanker payudara merupakan salah satu jenis kanker yang umum terjadi pada wanita di seluruh dunia. Deteksi dini kanker payudara sangat penting untuk meningkatkan tingkat kesembuhan. Mammography adalah metode pencitraan medis yang digunakan untuk deteksi dini kanker payudara. Dalam hal ini, teknologi Deep Learning dan pengklasifikasi terkomputerisasi, seperti Convolutional Neural Network (CNN) dengan model Resnet, telah digunakan dalam analisis dan prediksi gambar mammography dengan hasil yang menjanjikan. Studi-studi sebelumnya telah menunjukkan akurasi tinggi dalam klasifikasi massa payudara menjadi jinak atau ganas menggunakan CNN dan Resnet. Selain itu, CNN juga telah digunakan untuk klasifikasi kanker payudara ganas dan jinak, prediksi risiko kanker payudara, serta deteksi dan klasifikasi massa kanker payudara dengan tingkat akurasi yang memuaskan. Penggunaan Deep Learning dalam analisis citra medis, termasuk mammogram dan gambar X-ray, terbukti menjadi alat yang efektif dalam meningkatkan diagnosis dan pengobatan kanker. Data yang digunakan terdiri dari 322 gambar yang terbagi menjadi 7 kelas. Setelah dilakukan pengujian didapatkan akurasi sebesar 72% dengan perbandingan data uji dan data latih sebesar 90:10 dan nilai confusion matrix sehingga dapat disimpulkan bahwa metode Resnet mengindefikasi kanker payudara berdasarkan kelasnya

Automatic application watershed in early detection and classification masses in mammography image using machine learning methods

Mammogram images are used by radiologists for the diagnosis of breast cancer. However, the interpretation of these images remains difficult depending on the type of breast, especially those of dense breasts, which are difficult to read, as they may contain abnormal structures similar to normal breast tissue and could lead to a high rate of false positives and false negatives. In this paper, we present an efficient computer-aided diagnostic system for the detection and classification of breast masses. After removing noise and artefacts from the images using 2D median filtering, mathematical morphology and pectoral muscle removal by Hough's algorithm, the resulting image is used for breast mass segmentation using the watershed algorithm. Thus, after the segmentation, the help system extracts several data by the wavelet transform and the co-occurrence matrix (GLCM) to finally lead to a classification in terms of malignant and benign mass via the Support Vector Machine (SVM) classifier. This method was applied on 48 MLO images from the image base (mini-MIAS) and the results obtained from this proposed system is 93,75% in terms of classification rate, 88% in terms of sensitivity and a specificity of 94%

Improving Cancer Classification With Domain Adaptation Techniques

Background: As the quantity and complexity of oncological data continue to increase, machine learning (ML) has become an important tool in helping clinicians better understand malignancies and provide personalized care. Diagnostic image analysis, in particular, has benefited from the advent of ML methods to improve image classification and generate prognostic information from imaging collected in routine clinical practice [1-3]. Deep learning, a subset of ML, has especially achieved remarkable performance in medical imaging, including segmentation [4, 5], object detection, classification [6], and diagnosis [7]. Despite the notable success of deep learning computer vision models on oncologic imaging data, recent studies have identified notable weaknesses in deep learning models used on diagnostic images. Specifically, deep learning models have difficulty generalizing to data that was not well represented during training. One potential solution is the use of domain adaptation (DA) techniques, which improve the generalizability of a deep learning model trained on one domain to better generalize to data of a target domain. Techniques: In this study, we explain the efficacy of four common DA techniques – MMD, CORAL, iDANN, and AdaBN - used on deep learning models trained on common diagnostic imaging modalities in oncology. We used two datasets of mammographic imaging and CT scans to test the prediction accuracy of models using each of these DA techniques and compared them to the performance of transfer learning. Results: In the mammographic imaging data, MMD, CORAL, and iDANN increased the target test accuracy for all four domains. MMD increased target accuracies by 3.6 - 45%, CORAL by 4- 48%, and iDANN by 1.5-49.4%. For the CT scan dataset, MMD, CORAL, and iDANN increased the target test accuracy for all domains. MMD increased the target accuracy by 2.0 – 13.9%, CORAL by 2.4 - 15.8%, and iDANN by 2.0 – 11.1%. in both the mammographic imaging data and the CT scans, AdaBN performed worse or comparably to transfer learning. Conclusion: We found that DA techniques significantly improve model performance and generalizability. These findings suggest that there’s potential to further study how multiple DA techniques can work together and that these can potentially help us create more robust, generalizable models

Breast cancer diagnosis: a survey of pre-processing, segmentation, feature extraction and classification

Machine learning methods have been an interesting method in the field of medical for many years, and they have achieved successful results in various fields of medical science. This paper examines the effects of using machine learning algorithms in the diagnosis and classification of breast cancer from mammography imaging data. Cancer diagnosis is the identification of images as cancer or non-cancer, and this involves image preprocessing, feature extraction, classification, and performance analysis. This article studied 93 different references mentioned in the previous years in the field of processing and tries to find an effective way to diagnose and classify breast cancer. Based on the results of this research, it can be concluded that most of today’s successful methods focus on the use of deep learning methods. Finding a new method requires an overview of existing methods in the field of deep learning methods in order to make a comparison and case study

Computer aided diagnosis system for breast cancer using deep learning.

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous applications provided efficient solutions to assist radiologists and doctors for medical imaging analysis, which has remained the essence of the visual representation that is used to construct the final observation and diagnosis. Medical research in cancerology and oncology has been recently blended with the knowledge gained from computer engineering and data science experts. In this context, an automatic assistance or commonly known as Computer-aided Diagnosis (CAD) system has become a popular area of research and development in the last decades. As a result, the CAD systems have been developed using multidisciplinary knowledge and expertise and they have been used to analyze the patient information to assist clinicians and practitioners in their decision-making process. Treating and preventing cancer remains a crucial task that radiologists and oncologists face every day to detect and investigate abnormal tumors. Therefore, a CAD system could be developed to provide decision support for many applications in the cancer patient care processes, such as lesion detection, characterization, cancer staging, tumors assessment, recurrence, and prognosis prediction. Breast cancer has been considered one of the common types of cancers in females across the world. It was also considered the leading cause of mortality among women, and it has been increased drastically every year. Early detection and diagnosis of abnormalities in screened breasts has been acknowledged as the optimal solution to examine the risk of developing breast cancer and thus reduce the increasing mortality rate. Accordingly, this dissertation proposes a new state-of-the-art CAD system for breast cancer diagnosis that is based on deep learning technology and cutting-edge computer vision techniques. Mammography screening has been recognized as the most effective tool to early detect breast lesions for reducing the mortality rate. It helps reveal abnormalities in the breast such as Mass lesion, Architectural Distortion, Microcalcification. With the number of daily patients that were screened is continuously increasing, having a second reading tool or assistance system could leverage the process of breast cancer diagnosis. Mammograms could be obtained using different modalities such as X-ray scanner and Full-Field Digital mammography (FFDM) system. The quality of the mammograms, the characteristics of the breast (i.e., density, size) or/and the tumors (i.e., location, size, shape) could affect the final diagnosis. Therefore, radiologists could miss the lesions and consequently they could generate false detection and diagnosis. Therefore, this work was motivated to improve the reading of mammograms in order to increase the accuracy of the challenging tasks. The efforts presented in this work consists of new design and implementation of neural network models for a fully integrated CAD system dedicated to breast cancer diagnosis. The approach is designed to automatically detect and identify breast lesions from the entire mammograms at a first step using fusion models’ methodology. Then, the second step only focuses on the Mass lesions and thus the proposed system should segment the detected bounding boxes of the Mass lesions to mask their background. A new neural network architecture for mass segmentation was suggested that was integrated with a new data enhancement and augmentation technique. Finally, a third stage was conducted using a stacked ensemble of neural networks for classifying and diagnosing the pathology (i.e., malignant, or benign), the Breast Imaging Reporting and Data System (BI-RADS) assessment score (i.e., from 2 to 6), or/and the shape (i.e., round, oval, lobulated, irregular) of the segmented breast lesions. Another contribution was achieved by applying the first stage of the CAD system for a retrospective analysis and comparison of the model on Prior mammograms of a private dataset. The work was conducted by joining the learning of the detection and classification model with the image-to-image mapping between Prior and Current screening views. Each step presented in the CAD system was evaluated and tested on public and private datasets and consequently the results have been fairly compared with benchmark mammography datasets. The integrated framework for the CAD system was also tested for deployment and showcase. The performance of the CAD system for the detection and identification of breast masses reached an overall accuracy of 97%. The segmentation of breast masses was evaluated together with the previous stage and the approach achieved an overall performance of 92%. Finally, the classification and diagnosis step that defines the outcome of the CAD system reached an overall pathology classification accuracy of 96%, a BIRADS categorization accuracy of 93%, and a shape classification accuracy of 90%. Results given in this dissertation indicate that our suggested integrated framework might surpass the current deep learning approaches by using all the proposed automated steps. Limitations of the proposed work could occur on the long training time of the different methods which is due to the high computation of the developed neural networks that have a huge number of the trainable parameters. Future works can include new orientations of the methodologies by combining different mammography datasets and improving the long training of deep learning models. Moreover, motivations could upgrade the CAD system by using annotated datasets to integrate more breast cancer lesions such as Calcification and Architectural distortion. The proposed framework was first developed to help detect and identify suspicious breast lesions in X-ray mammograms. Next, the work focused only on Mass lesions and segment the detected ROIs to remove the tumor’s background and highlight the contours, the texture, and the shape of the lesions. Finally, the diagnostic decision was predicted to classify the pathology of the lesions and investigate other characteristics such as the tumors’ grading assessment and type of the shape. The dissertation presented a CAD system to assist doctors and experts to identify the risk of breast cancer presence. Overall, the proposed CAD method incorporates the advances of image processing, deep learning, and image-to-image translation for a biomedical application

Towards Secure and Intelligent Diagnosis: Deep Learning and Blockchain Technology for Computer-Aided Diagnosis Systems

Cancer is the second leading cause of death across the world after cardiovascular disease. The survival rate of patients with cancerous tissue can significantly decrease due to late-stage diagnosis. Nowadays, advancements of whole slide imaging scanners have resulted in a dramatic increase of patient data in the domain of digital pathology. Large-scale histopathology images need to be analyzed promptly for early cancer detection which is critical for improving patient's survival rate and treatment planning. Advances of medical image processing and deep learning methods have facilitated the extraction and analysis of high-level features from histopathological data that could assist in life-critical diagnosis and reduce the considerable healthcare cost associated with cancer. In clinical trials, due to the complexity and large variance of collected image data, developing computer-aided diagnosis systems to support quantitative medical image analysis is an area of active research. The first goal of this research is to automate the classification and segmentation process of cancerous regions in histopathology images of different cancer tissues by developing models using deep learning-based architectures. In this research, a framework with different modules is proposed, including (1) data pre-processing, (2) data augmentation, (3) feature extraction, and (4) deep learning architectures. Four validation studies were designed to conduct this research. (1) differentiating benign and malignant lesions in breast cancer (2) differentiating between immature leukemic blasts and normal cells in leukemia cancer (3) differentiating benign and malignant regions in lung cancer, and (4) differentiating benign and malignant regions in colorectal cancer. Training machine learning models, disease diagnosis, and treatment often requires collecting patients' medical data. Privacy and trusted authenticity concerns make data owners reluctant to share their personal and medical data. Motivated by the advantages of Blockchain technology in healthcare data sharing frameworks, the focus of the second part of this research is to integrate Blockchain technology in computer-aided diagnosis systems to address the problems of managing access control, authentication, provenance, and confidentiality of sensitive medical data. To do so, a hierarchical identity and attribute-based access control mechanism using smart contract and Ethereum Blockchain is proposed to securely process healthcare data without revealing sensitive information to an unauthorized party leveraging the trustworthiness of transactions in a collaborative healthcare environment. The proposed access control mechanism provides a solution to the challenges associated with centralized access control systems and ensures data transparency and traceability for secure data sharing, and data ownership