Comprehensive Study for Breast Cancer Using Deep Learning and Traditional Machine Learning

A computational framework is developed which couples a series of models, each describing vastly different physical events, in order to characterize particle growth (agglomeration) in thermochemically reacting granular flows. The modelling is purposely simplified to expose the dominant mechanisms which control agglomeration. The overall system is comprised of relatively simple coupled submodels describing impact, heat production, bonding and fragmentation, each of which can be replaced by more elaborate descriptions, if and when they are available. Inverse problems, solved with a genetic algorithm, are then constructed to ascertain system parameters which maximize agglomeration likelihood within a range of admissible data.

Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review

Deep learning has the capacity to gain great accuracy of diagnosing of numerous types of cancers, along with lung, cervical, colon, and breast cancer. It builds an efficient algorithm based on multiple processing (hidden) layers of neurons. Manual assessment of Cancer using Medical Image (CT images) requires expensive human labors and can easily cause the misdiagnose of any type of cancer. The Researcher focus on automatically diagnosing cancer by using the deep learning technique. Breast cancer is a particularly common sickness among women and maximum associated cause of female mortality. The survival rate of breast cancer patients can be expanded with the aid of powerful treatment, which can initiate upon early prognosis of the disease. This chapter introduces Deep Learning under medical image processing to analysis and diagnosis of Cancer (Ehteshami Bejnordi et al., in Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images, pp. 929–932, 2017 [1]). Identification of most cancer might facilitate in sparing a massive wide variety of lives over the globe community and deep neural networks may be correctly used for intelligent image analysis. The essential structure of how this deep learning takes a shot at medical image processing (Litjens et al. in A survey on deep learning in medical image analysis, 2017, [2]; Rezaeilouyeh et al. in J Med Imaging 3(4):044501, 2016 [3]) is furnished in this research, i.e., pre-processing, image segmentation and post-processing. The following piece of this part depicts the rudiments of the field of disease conclusion, which incorporates steps of malignant growth determination followed by the regular arrangement strategies utilized by specialists, giving a verifiable thought of disease grouping methods to the readers. Next an attempt has been made to classify the extracted features from mammograms as benign or malignant by using Convolutional neural network (CNN) (Ciresan in Mitosis detection in breast cancer histology images with deep neural networks. Springer, Berlin, pp. 411–418, 2013 [4]; LeCun et al. in International symposium on circuits and systems, pp. 253–256, 2010 [5]; Huynh et al. in J Med Imaging 3(3):034501, 2016 [6]) is applied to classify cancer using optimal features obtained from cell segmented images. Performance improvised of the approaches by varying various parameters is studied.

In this paper a new mathematical and computational framework for boundary value problems described by self-adjoint differential operators is presented. In this framework, numerically computed solutions, when converged, possess the same degree of global smoothness in terms of differentiability up to any desired order as the theoretical solutions. This is accomplished using spaces Ĥk,p that contain basis functions of degree p and order k - 1 (or the order of the space k). It is shown that the order of space k is an intrinsically important independent parameter in all finite element computational processes in addition to the discretization characteristic length h and the degree of basis functions p when the theoretical solutions are analytic. Thus, in all finite element computations, all quantities of interest (e.g., quadratic functional, error or residual functional, norms and seminorms, error norms, etc.) are dependent on h, p as well as k. Therefore, for fixed h and p, convergence of the finite element process can also be investigated by changing k, hence k-convergence and thus the k-version of finite element method. With h, p, and k as three independent parameters influencing all finite element processes, we now have k, hk, pk, and hpk versions of finite element methods. The issue of minimally conforming finite element spaces is reexamined and it is demonstrated that the definition of currently believed minimally conforming space which permit weak convergence of the highest-order derivatives of the dependent variables appearing in the bilinear form is not justifiable mathematically or from physics view point. A new criterion is proposed for establishing the minimally conforming spaces which is more in agreement with the physics and mathematics of the BVP. Significant features and merits of the proposed mathematical and computational framework are presented, discussed, illustrated, and substantiated mathematically as well as numerically with the Galerkin and least-squares finite element formulations for self-adjoint boundary-value problems.

Focus issue: Artificial intelligence in medical physics.

In the companion papers [1,2], authors introduced the concepts of k-version of finite element method and k, hk, pk, hkp-processes of the finite element method for boundary value problems described by self-adjoint and non-self adjoint operators using Ĥk,p(Ω) spaces with specific details including numerical studies for weak forms and least square processes. It was demonstrated that a variationally consistent (VC) weak form is possible when the differential operator is self-adjoint, however, in case of non-self-adjoint operators the weak forms are variationally inconsistent (VIC) which lead to degenerate computational processes that can produce spurious oscillations in the computed solutions. In this paper we demonstrate that when the boundary value problems are described by non-linear differential operators, Galerkin processes and weak forms can never be variationally consistent and hence result in degenerate computational processes and suffer from same problems as in the case of non-self-adjoint operators ...

Breast cancer is a pervasive global health concern, impacting millions of women worldwide. Timely detection and precise diagnosis are pivotal factors in improving patient outcomes. This review presents a comprehensive analysis of machine learning (ML), deep learning (DL), and transfer learning (TL) models applied to breast cancer detection. It encompasses the classification of different types of breast cancer, prognosis, diagnosis, prediction, and clinical decision support. The present study examines a wide range of articles to recognize the frequently used architectures, datasets, activation functions, and evaluation metrics. Furthermore, the review scrutinizes the effectiveness of various AI techniques in predicting and diagnosing breast cancer, elucidating various evaluation metrics and their utilization. The WDBC and BreakHis databases are image datasets commonly used for breast cancer prediction. The performance of machine learning, deep learning, and transfer learning algorithms varies significantly in terms of precision, recall, F1 score, and accuracy. CNN model is the most commonly used deep learning technique, with the study indicating that it is used by about 60% of researchers. In terms of network architecture, ResNet is used by about 57% of researchers. Conspicuously, Softmax occurs as the most frequently used activation function i.e., 89%, and accuracy and precision are the foremost metrics for performance evaluation i.e., 60%. According to the study, deep learning and transfer learning methods achieve the highest accuracy, reaching 99.54% in breast cancer detection which raises concerns about dataset bias, overfitting, and lack of external validation. In terms of machine learning based breast cancer detection, the random forest algorithm demonstrates remarkable success, achieving the highest accuracy rate of 99%. This review serves as a comprehensive exploration of the current state of AI applications in breast cancer, highlighting their potential to reshape the landscape of breast cancer healthcare.

Breast cancer is a major public health concern, and early diagnosis and classification are critical for effective treatment. Machine learning and deep learning techniques have shown great promise in the classification and diagnosis of breast cancer. In this review, we examine studies that have used these techniques for breast cancer classification and diagnosis, focusing on five groups of medical images: mammography, ultrasound, MRI, histology, and thermography. We discuss the use of five popular machine learning techniques, including Nearest Neighbor, SVM, Naive Bayesian Network, DT, and ANN, as well as deep learning architectures and convolutional neural networks. Our review finds that machine learning and deep learning techniques have achieved high accuracy rates in breast cancer classification and diagnosis across various medical imaging modalities. Furthermore, these techniques have the potential to improve clinical decision-making and ultimately lead to better patient outcomes.

Breast cancer is a common cause of death among women worldwide. Ultrasonic imaging is a valuable diagnostic tool in breast cancer detection. However, the accuracy of computer-aided diagnosis systems for breast cancer classification is limited due to the lack of well-annotated datasets. This study proposes a deep learning-based framework for breast mass classification using ultrasound images, which incorporates a novel data augmentation technique, Generative Adversarial Network (GAN), and Transfer Learning (TL). Automating early tumor identification and classification in breast cancer diagnosis can save lives by improving the accuracy of diagnoses and reducing the need for invasive procedures. However, the limited availability of well-annotated datasets for ultrasound images of breast cancer has hampered the development of accurate computer-aided diagnosis systems. The accuracy of breast mass classification using ultrasound images is limited due to the lack of well-annotated datasets. Conventional data augmentation techniques have limitations in applications with strict guidelines, such as medical datasets. Therefore, there is a need to develop a novel data augmentation technique to improve the accuracy of breast mass classification using ultrasound images. The proposed framework can be extended to other medical imaging applications, where the availability of well-annotated datasets is limited. The GAN-based data augmentation technique and TL-based feature extraction can be used to improve the accuracy of classification models in other medical imaging applications. Additionally, the proposed framework can be used to develop accurate computer-aided diagnosis systems for breast cancer detection in clinical settings. The proposed framework incorporates a deep learning-based approach for breast mass classification using ultrasound images. The framework includes a GAN-based data augmentation technique and TL for feature extraction. The dataset used for training and testing the model is the Breast Ultrasound Images (BUSI) dataset, which includes 1311 images with normal and abnormal breast masses. The proposed framework achieved an accuracy of 99.6% for breast mass classification using ultrasound images, which outperformed existing methods. The GAN-based data augmentation technique and TL-based feature extraction improved the accuracy of the classification model. The results suggest that deep learning algorithms can be effectively applied for breast ultrasound categorization. The proposed framework presents a novel approach for breast mass classification using ultrasound images, which incorporates a GAN-based data augmentation technique and TL-based feature extraction. The results demonstrate that the proposed framework outperforms existing methods and achieves high accuracy in breast mass classification using ultrasound images. This framework can be useful for developing accurate computer-aided diagnosis systems for breast cancer detection.

Analysis of tissue and arterial blood temperatures in the resting human forearm. 1948.

Every year, millions of women across the globe are diagnosed with breast cancer (BC), an illness that is both common and potentially fatal. To provide effective therapy and enhance patient outcomes, it is essential to make an accurate diagnosis as soon as possible. In recent years, deep-learning (DL) approaches have shown great effectiveness in a variety of medical imaging applications, including the processing of histopathological images. Using DL techniques, the objective of this study is to recover the detection of BC by merging qualitative and quantitative data. Using deep mutual learning (DML), the emphasis of this research was on BC. In addition, a wide variety of breast cancer imaging modalities were investigated to assess the distinction between aggressive and benign BC. Based on this, deep convolutional neural networks (DCNNs) have been established to assess histopathological images of BC. In terms of the Break His-200×, BACH, and PUIH datasets, the results of the trials indicate that the level of accuracy achieved by the DML model is 98.97%, 96.78, and 96.34, respectively. This indicates that the DML model outperforms and has the greatest value among the other methodologies. To be more specific, it improves the results of localization without compromising the performance of the classification, which is an indication of its increased utility. We intend to proceed with the development of the diagnostic model to make it more applicable to clinical settings.

Deep learning is a branch of machine learning that has grown by leaps and bounds since it was first used in computer vision. The "Olympics" of computer vision, ImageNet Classification, was won by a system that used deep learning and convolutional neural networks in December 2012. Because of how important it is in the field, this competition is sometimes called the "Olympics" of computer vision. (CNN). Since then, people in many different fields, such as medical image analysis, have looked into deep learning. We are going to look into whether or not it would be possible to use deep learning algorithms to analyse medical images. This poll asked people what they thought about the four following topics related to machine learning: 1) How it is now used in computer vision, 2) How machine learning has changed before and after deep learning, 3) What role ML models play in deep learning, and 4) How deep learning can be used to analyse medical photos. Before the invention of deep learning, most machine learning systems relied on inputs called "features." This type of machine learning is called feature-based ML by some (also known as feature-based ML). Studying photographic data can be used to learn through deep learning without the need to separate objects or pull out features. The main difference between the two was this. This was pretty clear when we looked at MLs made before and after deep learning became very popular. This part, along with the model's huge scope, makes deep learning work well. Even though the term "deep learning" is still new, a study on the topic found that photo-input deep-learning algorithms have been available in the field of machine learning for a long time. Even though "deep learning" is a term that has only been around for a short time, this was seen. Even though the idea of "deep learning" is still in its early stages, discoveries like this one have been made. Even before the term "deep learning" was invented, machine learning techniques that used pictures as input were already showing promise for solving a wide range of medical image interpretation problems. Even before the term "deep learning" was made up, this was the case. One of these jobs is to Figure out how lesions are different from other organs and tissues. To solve the problem, an approach to machine learning that is based on images was used. In the next few decades, it is expected that deep learning will completely replace all of the traditional ways that medical images are currently interpreted. This is because applying deep learning and other machine learning techniques to the study of picture data could make medical image analysis much better. "Deep learning," which is the process of teaching computers to "learn" from images, is one of the most promising and quickly growing areas of medical image analysis. Traditional ways of figuring out what a medical image means are likely to be replaced in the next few decades by machine learning that works from pictures.

Objectives: The research aims to enhance breast cancer detection accuracy and effectiveness using deep transfer learning and pre-trained neural networks. It analyses breast ultrasound images and identifies important characteristics using pre-trained networks. The goal is to create a more efficient and accurate automated system for breast cancer detection. Methods: The study uses breast ultrasound cancer image data from the Kaggle Data Repository to extract informative features, identify cancer-related characteristics, and classify them into benign, malignant, and normal tissue. Pre-trained Deep Neural Networks (DNNs) extract these features and feed them into a 10-fold cross-validation SVM classifier. The SVM is evaluated using various kernel functions to identify the best kernel for separating data points. This methodology aims to achieve accurate classification of breast cancer in ultrasound images. Findings: The study confirms the effectiveness of deep transfer learning for breast cancer detection in ultrasound images, with Inception V3 outperforming VGG-16 and VGG-19 in extracting relevant features. The combination of Inception V3 and the SVM classifier with a polynomial kernel achieved the highest classification accuracy, indicating its ability to model complex relationships. The study demonstrated an AUC of 0.944 and a classification accuracy of 87.44% using the Inception V3 + SVM polynomial. Novelty: This research demonstrates the potential of deep transfer learning and SVM classifiers for accurate breast cancer detection in ultrasound images. It integrates Inception V3, VGG-16, and VGG-19 for breast cancer detection, demonstrating improved classification accuracy. The combination of Inception V3 and SVM (polynomial) achieved a significant AUC (0.944) and classification accuracy (87.44%), outperforming other models tested. This research underscores the potential of these technologies for accurate breast cancer detection in ultrasound images. Keywords: Breast Cancer, Deep Learning, Feature Extraction, Inception-v3, SVM, Transfer Learning

Chapter 4 - Breast cancer detection from mammograms using artificial intelligence

Predicting Atrial Fibrillation from Automated Measurements of Left Atrial Volume Using Routine Chest CT Examination: Overlooked and Underrecognized Risk Factors.