Artificial Intelligence Techniques for Cancer Detection and Classification: Review Study

Cancer is the general name for a group of more than 100 diseases. Although cancer includes different types of diseases, they all start because abnormal cells grow out of control. Without treatment, cancer can cause serious health problems and even loss of life. Early detection of cancer may reduce mortality and morbidity. This paper presents a review of the detection methods for lung, breast, and brain cancers. These methods used for diagnosis include artificial intelligence techniques, such as support vector machine neural network, artificial neural network, fuzzy logic, and adaptive neuro-fuzzy inference system, with medical imaging like X-ray, ultrasound, magnetic resonance imaging, and computed tomography scan images. Imaging techniques are the most important approach for precise diagnosis of human cancer. We investigated all these techniques to identify a method that can provide superior accuracy and determine the best medical images for use in each type of cancer

A survey on computational intelligence approaches for predictive modeling in prostate cancer

Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex forconventional statistical techniques to process quickly and eciently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes evolutionary algorithms (also known asmetaheuristic optimisation, nature inspired optimisation algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these,as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Implementing decision tree-based algorithms in medical diagnostic decision support systems

As a branch of healthcare, medical diagnosis can be defined as finding the disease based on the signs and symptoms of the patient. To this end, the required information is gathered from different sources like physical examination, medical history and general information of the patient. Development of smart classification models for medical diagnosis is of great interest amongst the researchers. This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database. Hence, classifying the medical datasets using smart techniques paves the way to design more efficient medical diagnostic decision support systems. Several databases have been provided in the literature to investigate different aspects of diseases. As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) for the development of classification models that can be implemented in computer-aided diagnosis systems. As a decision tree (DT), CART is fast to create, and it applies to both the quantitative and qualitative data. For classification problems, RF and ET employ a number of weak learners like CART to develop models for classification tasks. We employed Wisconsin Breast Cancer Database (WBCD), Z-Alizadeh Sani dataset for coronary artery disease (CAD) and the databanks gathered in Ghaem Hospital’s dermatology clinic for the response of patients having common and/or plantar warts to the cryotherapy and/or immunotherapy methods. To classify the breast cancer type based on the WBCD, the RF and ET methods were employed. It was found that the developed RF and ET models forecast the WBCD type with 100% accuracy in all cases. To choose the proper treatment approach for warts as well as the CAD diagnosis, the CART methodology was employed. The findings of the error analysis revealed that the proposed CART models for the applications of interest attain the highest precision and no literature model can rival it. The outcome of this study supports the idea that methods like CART, RF and ET not only improve the diagnosis precision, but also reduce the time and expense needed to reach a diagnosis. However, since these strategies are highly sensitive to the quality and quantity of the introduced data, more extensive databases with a greater number of independent parameters might be required for further practical implications of the developed models

Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

Applications of computational methods in biomedical breast cancer imaging diagnostics: A review

With the exponential increase in new cases coupled with an increased mortality rate, cancer has ranked as the second most prevalent cause of death in the world. Early detection is paramount for suitable diagnosis and effective treatment of different kinds of cancers, but this is limited to the accuracy and sensitivity of available diagnostic imaging methods. Breast cancer is the most widely diagnosed cancer among women across the globe with a high percentage of total cancer deaths requiring an intensive, accurate, and sensitive imaging approach. Indeed, it is treatable when detected at an early stage

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page