Enhanced Artificial Intelligence System for Diagnosing and Predicting Breast Cancer Using Deep Learning

Breast cancer is the leading cause of death among women with cancer. Computer-aided diagnosis is an efficient method for assisting medical experts in early diagnosis, improving the chance of recovery. Employing artificial intelligence (AI) in the medical area is very crucial due to the sensitivity of this field. This means that the low accuracy of the classification methods used for cancer detection is a critical issue. This problem is accentuated when it comes to blurry mammogram images. In this paper, convolutional neural networks (CNNs) are employed to present the traditional convolutional neural network (TCNN) and supported convolutional neural network (SCNN) approaches. The TCNN and SCNN approaches contribute by overcoming the shift and scaling problems included in blurry mammogram images. In addition, the flipped rotation-based approach (FRbA) is proposed to enhance the accuracy of the prediction process (classification of the type of cancerous mass) by taking into account the different directions of the cancerous mass to extract effective features to form the map of the tumour. The proposed approaches are implemented on the MIAS medical dataset using 200 mammogram breast images. Compared to similar approaches based on KNN and RF, the proposed approaches show better performance in terms of accuracy, sensitivity, spasticity, precision, recall, time of performance, and quality of image metrics

Data Mining and optimization of mathematical models in biomedical engineering

Ateroskleroza je oboljenje arterija koje karakteriše smanjenje lumena krvnog suda ograničavajući na taj način dotok krvi i kiseonika do određenih organa. Kao posledica ateroskleroze može doći do moždanog udara, ishemijske bolesti srca ili srčanog udara, pa je uspešno lečenje ateroskleroze od velikog značaja kako bi se izbegle eventualne fatalne posledice. U okviru ove disertacije su prikazani različiti matematički modeli za simulaciju nastanka i razvoja ateroskleroze koji su optimizovani prema dostupnim eksperimentalnim podacima. Optimizovani matematički modeli mogu biti od velike koristi jer mogu lekarima pružiti uvid u dalji razvoj bolesti i pomoći im na taj način u izboru najbolje terapije. Brojna literatura pokazuje da na proces nastanka ateroskleroze utiču brojni hemodinamički faktori među kojima je najvažniji smičući napon na zidu arterije. Zone arterije sa niskim vrednostima smičućeg napona na zidu su visoko rizične za nastanak ateroskleroze. Sa druge strane, ekstremno visoke vrednosti smičućeg napona na zidu mogu dovesti do destabilizacije već postojećeg plaka. Prema tome, poznavanje raspodele smičućeg napona na zidu arterije može biti od velikog značaja. Metodom konačnih elemenata moguće je veoma precizno izračunati raspodelu smičućeg napona na zidu posmatrane arterije. Ovo međutim zahteva u nekim situacijama mnogo vremena pa se dovodi u pitanje upotreba u realnim kliničkim situacijama kada je rezultate potrebno prikazati u kratkom roku. Rešenje može biti bazirano na tehnikama istraživanja podataka koje sa prihvatljivom tačnošću, gotovo trenutno mogu predviđati raspodelu smičućeg napona za datu arteriju. Ova metodologija je verifikovana za geometrijski parametrizovane modele karotidne bifurkacije i aneurizme. Pored ateroskleroze još jedna opaka bolest je kancer. Kancer dojke je jedan od najčešćih oblika kancera koji se javlja kod žena. Mamografija je neinvazivna, rendgenska metoda za pregled dojki koji omogućava detekciju masa u ranoj fazi. Međutim, čak i najiskusniji lekari ponekad imaju problema prilikom pregleda mamografa. Primenom naprednih metodologija pretprocesiranja, segmentacije i tehnika istraživanja podataka kreiran je sistem kompjuterski pomognute dijagnoze za detekciju tumora na mamografima.Atherosclerosis is a disease of the arteries characterized by the lumen decrease of the blood vessel, thus limiting the blood flow and oxygen supply to certain organs. As a result, atherosclerosis can lead to stroke, ischemic heart disease or heart attack, thus the successful treatment of atherosclerosis is of great importance in order to avoid possible fatal consequences. Within this thesis, different mathematical models for atherosclerosis initiation and development are presented and optimized by the use of available experimental data. These optimized mathematical models can be of a great importance providing to the physicians an overview of the future disease development and assisting them in choosing the best therapy. Numerous studies show that the process of atherosclerosis initiation is affected by numerous hemodynamic factors among which wall shear stress is the most important. Low wall shear stress areas are the ones with a high risk for atherosclerosis initiation and development. On the other hand, extremely high wall shear stress can cause destabilization of the existing plaque. Thus, the knowledge of the wall shear stress distribution is of a great importance. By using finite element method, it is possible to calculate precisely the wall shear stress distribution of the observed artery. However, this process can be time consuming sometimes calling into question the application in real clinical situations where the results should be presented in a very short period of time. The solution can be seen in data mining methodologies which can accurately enough predict the wall shear stress distribution of the observed artery instantly. This methodology is verified for geometrically parameterized models of carotid artery bifurcation and aneurysm. In addition to atherosclerosis, another severe disease is cancer. Breast cancer is the most common type of cancer that occurs in women. Mammography is a non-invasive, x-ray method for breast examination, which allows the detection of masses in early stages. However, even the most experienced physicians sometimes have trouble reading the mammogram. By the application of advanced preprocessing, segmentation and data mining methodologies, the computer aided system for tumor detection in mammograms is created

Application of Data Mining Algorithms for Mammogram Classification

One of the leading causes of cancer death among women is breast cancer. In our work we aim at proposing a prototype of a medical expert system (based on data mining techniques) that could significantly aid medical experts to detect breast cancer. This paper presents the CAD (computer aided diagnosis) system for the detection of normal and abnormal pattern in the breast. The proposed system consists of four major steps: the image preprocessing, the feature extraction, the feature selection and the classification process that classifies mammogram into normal (without tumor) and abnormal (with tumor) pattern. After removing noise from mammogram using the Discrete Wavelet Transformation (DWT), first is selected the region of interest (ROI). By identifying the boundary of the breast, it is possible to remove any artifact present outside the breast area, such as patient markings. Then, a total of 20 GLCM features are extracted from the ROI, which were used as inputs for classification algorithms. In order to compare the classification results, we used seven different classifiers. Normal breast images and breast image with masses (total 322 images) used as input images in this study are taken from the mini-MIAS database

Application of Data Mining Algorithms for Mammogram Classification

One of the leading causes of cancer death among women is breast cancer. In our work we aim at proposing a prototype of a medical expert system (based on data mining techniques) that could significantly aid medical experts to detect breast cancer. This paper presents the CAD (computer aided diagnosis) system for the detection of normal and abnormal pattern in the breast. The proposed system consists of four major steps: the image preprocessing, the feature extraction, the feature selection and the classification process that classifies mammogram into normal (without tumor) and abnormal (with tumor) pattern. After removing noise from mammogram using the Discrete Wavelet Transformation (DWT), first is selected the region of interest (ROI). By identifying the boundary of the breast, it is possible to remove any artifact present outside the breast area, such as patient markings. Then, a total of 20 GLCM features are extracted from the ROI, which were used as inputs for classification algorithms. In order to compare the classification results, we used seven different classifiers. Normal breast images and breast image with masses (total 322 images) used as input images in this study are taken from the mini-MIAS database

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF