Comparison of GLCM and First Order Feature Extraction Methods for Classification of Mammogram Images

Breast cancer is one of the main causes of death in women and ranks first in cancer cases in Indonesia. Therefore, an early detection and prevention of breast cancer is necessary, one of which is through mammography procedures. A machine learning classifier such as Support Vector Machines (SVM) could be used as an aid to the doctors and radiologist in diagnosing breast cancer from the mammogram images. The aim of this paper is to compare two feature extraction methods used in SVM, namely the Gray Level Co-Occurrence Matrix (GLCM) and first order with two kernels for each method, namely Gaussian and Polynomial. Classification using SVM method is carried out by testing several parameters such as the value of C, gamma, degree and varying the pixel spacing values ​​in GLCM, which usually in previous studies only used the default pixel spacing. The dataset consists of 500 mammogram images containing 250 benign and malignant images, respectively. This study is expected to find out the best method with the highest accuracy between these two texture feature extractions and and able to distinguish between benign and malignant classes correctly. The result achieved that Gray Level Co-Occurrence Matrix (GLCM) feature extraction method with both Gaussian and Polynomial kernel yields the best performance with an accuracy of 89%

A Review on Data Fusion of Multidimensional Medical and Biomedical Data

Data fusion aims to provide a more accurate description of a sample than any one source of data alone. At the same time, data fusion minimizes the uncertainty of the results by combining data from multiple sources. Both aim to improve the characterization of samples and might improve clinical diagnosis and prognosis. In this paper, we present an overview of the advances achieved over the last decades in data fusion approaches in the context of the medical and biomedical fields. We collected approaches for interpreting multiple sources of data in different combinations: image to image, image to biomarker, spectra to image, spectra to spectra, spectra to biomarker, and others. We found that the most prevalent combination is the image-to-image fusion and that most data fusion approaches were applied together with deep learning or machine learning methods

Augmented breast tumor classification by perfusion analysis

Magnetic resonance and computed tomography imaging aid in the diagnosis and analysis of pathologic conditions. Blood flow, or perfusion, through a region of tissue can be computed from a time series of contrast-enhanced images. Perfusion is an important set of physiological parameters that reflect angiogenesis. In cancer, heightened angiogenesis is a key process in the growth and spread of tumorous masses. An automatic classification technique using recovered perfusion may prove to be a highly accurate diagnostic tool. Such a classification system would supplement existing histopathological tests, and help physicians to choose the most optimal treatment protocol. Perfusion is obtained through deconvolution of signal intensity series and a pharmacokinetic model. However, many computational problems complicate the accurate-consistent recovery of perfusion. The high time-resolution acquisition of images decreases signal-to-noise, producing poor deconvolution solutions. The delivery of contrast agent as a function of time must also be determined or sampled before deconvolution can proceed. Some regions of the body, such as the brain, provide a nearby artery to serve as this arterial input function. Poor estimates can lead to an over or under estimation of perfusion. Breast tissue is an example of one tissue region where a clearly defined artery is not present. This proposes a new method of using recovered perfusion and spatial information in an automated classifier. This classifier grades suspected lesions as benign or malignant. This method can be integrated into a computer-aided diagnostic system to enhance the value of medical imagery

Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH): A Novel Feature Extraction Technique

In this paper, we present a novel feature extraction technique, termed Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH), and exploit it to detect breast cancer in volumetric medical images. The technique is incorporated as part of an intelligent expert system that can aid medical practitioners making diagnostic decisions. Analysis of volumetric images, slice by slice, is cumbersome and inefficient. Hence, 3D-LESH is designed to compute a histogram-based feature set from a local energy map, calculated using a phase congruency (PC) measure of volumetric Magnetic Resonance Imaging (MRI) scans in 3D space. 3D-LESH features are invariant to contrast intensity variations within different slices of the MRI scan and are thus suitable for medical image analysis.The contribution of this article is manifold. First, we formulate a novel 3D-LESH feature extraction technique for 3D medical images to analyse volumetric images. Further, the proposed 3D-LESH algorithmis, for the first time, applied to medical MRI images. The final contribution is the design of an intelligent clinical decision support system (CDSS) as a multi-stage approach, combining novel 3D-LESH feature extraction with machine learning classifiers, to detect cancer from breast MRI scans. The proposed system applies contrast-limited adaptive histogram equalisation (CLAHE) to the MRI images before extracting 3D-LESH features. Furthermore, a selected subset of these features is fed into a machine-learning classifier, namely, a support vector machine (SVM), an extreme learning machine (ELM) or an echo state network (ESN) classifier, to detect abnormalities and distinguish between different stages of abnormality. We demonstrate the performance of the proposed technique by its application to benchmark breast cancer MRI images. The results indicate high-performance accuracy of the proposed system (98%±0.0050, with an area under a receiver operating charactertistic curve value of 0.9900 ± 0.0050) with multiple classifiers. When compared with the state-of-the-art wavelet-based feature extraction technique, statistical analysis provides conclusive evidence of the significance of our proposed 3D-LESH algorithm

Developing of Ultrasound Experimental Methods using Machine Learning Algorithms for Application of Temperature Monitoring of Nano-Bio-Composites Extrusion

In industry fiber degradation during processing of biocomposite in the extruder is a problem that requires a reliable solution to save time and money wasted on producing damaged material. In this thesis, We try to focus on a practical solution that can monitor the change in temperature that causes fiber degradation and material damage to stop it when it occurs. Ultrasound can be used to detect the temperature change inside the material during the process of material extrusion. A monitoring approach for the extruder process has been developed using ultrasound system and the techniques of machine learning algorithms. A measurement cell was built to form a dataset of ultrasound signals at different temperatures for analysis. Machine learning algorithms were applied through machine-learning algorithm’s platform to classify the dataset based on the temperature. The dataset was classified with accuracy 97% into two categories representing over and below damage temperature (190oc) ultrasound signal. This approach could be used in industry to send an alarm or a temperature control signal when material damage is detected. Biocomposite is at the core of automotive industry material research and development concentration. Melt mixing process was used to mix biocomposite material with multi-walled carbon nanotubes (MWCNTs) for the purpose of enhancing mechanical and thermal properties of biocomposite. The resulting composite nano-bio- composite was tested via different types of thermal and mechanical tests to evaluate its performance relative to biocomposite. The developed material showed enhancement in mechanical and thermal properties that considered a high potential for applications in the future

Artificial Intelligence and Medicine

The introduction of artificial intelligence (AI) has resulted in numerous technological advancements in the medical profession and a radical transformation of the old medical model. Artificial intelligence in medicine consists mostly of machine learning, deep learning, expert systems, intelligent robotics, the internet of medical things, and other prevalent and new AI technology. The primary applications of AI in the medical industry are intelligent screening, intelligent diagnosis, risk prediction, and supplemental treatment. Presently, medical AI has achieved significant advances, and big data quality management, new technology empowerment innovation, multi-domain knowledge integration, and personalized medical decision-making will exhibit greater growth potential in the clinical arena