Wyniki wyszukiwania - BazTech

1

Lung cancer detection using an integration of fuzzy K-Means clustering and deep learning techniques for CT lung images

Prasad J. Maruthi Nagendra, Chakravarty S., Krishna M. Vamsi

Bulletin of the Polish Academy of Sciences. Technical Sciences

|

2022

|

Vol. 70, nr 3

art. no. e139006

EN

Computer aided detection systems are used for the provision of second opinion during lung cancer diagnosis. For early-stage detection and treatment false positive reduction stage also plays a vital role. The main motive of this research is to propose a method for lung cancer segmentation. In recent years, lung cancer detection and segmentation of tumors is considered one of the most important steps in the surgical planning and medication preparations. It is very difficult for the researchers to detect the tumor area from the CT (computed tomography) images. The proposed system segments lungs and classify the images into normal and abnormal and consists of two phases, The first phase will be made up of various stages like pre-processing, feature extraction, feature selection, classification and finally, segmentation of the tumor. Input CT image is sent through the pre-processing phase where noise removal will be taken care of and then texture features are extracted from the pre-processed image, and in the next stage features will be selected by making use of crow search optimization algorithm, later artificial neural network is used for the classification of the normal lung images from abnormal images. Finally, abnormal images will be processed through the fuzzy K-means algorithm for segmenting the tumors separately. In the second phase, SVM classifier is used for the reduction of false positives. The proposed system delivers accuracy of 96%, 100% specificity and sensitivity of 99% and it reduces false positives. Experimental results shows that the system outperforms many other systems in the literature in terms of sensitivity, specificity, and accuracy. There is a great tradeoff between effectiveness and efficiency and the proposed system also saves computation time. The work shows that the proposed system which is formed by the integration of fuzzy K-means clustering and deep learning technique is simple yet powerful and was effective in reducing false positives and segments tumors and perform classification and delivers better performance when compared to other strategies in the literature, and this system is giving accurate decision when compared to human doctor’s decision.

2

Multi-channeled MR brain image segmentation: A novel double optimization approach combined with clustering technique for tumor identification and tissue segmentation

Narayanan Anitha, Rajasekaran M. Pallikonda, Zhang Yudong, Govindaraj Vishnuvarthanan, Thiyagarajan Arunprasath

Biocybernetics and Biomedical Engineering

|

2019

|

Vol. 39, no. 2

350--381

EN

Growth of cancer cells within the human body is a major outcome of the manipulation of cells and it has resulted in the deterioration of the life span of humans. The impact of cancer cells is irretrievable and it has paved the way to the formation of tumors within the human body. For achieving and developing a single-structured framework to prominently identify the tumor regions and segmenting the tissue structures specifically in human brain, a novel combinational algorithm is proposed through this paper. The algorithm has been embodied with two optimization techniques namely particle swarm optimization (PSO) and bacteria foraging optimization (BFO), wherein, PSO helps in finding the best position of global bacterium for BFO, consecutively, BFO supports the modified fuzzy c means (MFCM) algorithm by providing optimized cluster heads. Finally, MFCM segments the tissue regions and identifies the tumor portion, thereby reducing the interaction and complication experienced by a radiologist during patient diagnosis. The strength of the proposed algorithm is proven by comparing it with the state-of-the-art techniques by means of evaluation parameters like mean squared error (MSE), peak signal to noise ratio (PSNR), sensitivity, specificity, etc., Data sets used in this paper were exclusively obtained from hospital, Brain web simulator and BRATS-2013 challenge. The sensitivity and specificity values for 115 MR brain slice images.

3

Multi-step process in computer assisted diagnosis of posterior cruciate ligaments

Zarychta P.

Biocybernetics and Biomedical Engineering

|

2016

|

Vol. 36, no. 4

657--669

EN

A multi-step methodology resulting in a three-dimensional visualization and construction of feature vector of posterior cruciate ligament is presented. In the first step the location of the posterior cruciate ligament is established using the fuzzy image concept. The fuzzy image concept is based on the entropy measure of fuzziness extended to two dimensions. In order to reduce the area of analysis, the region of interest including the ligament structures is detected. In this case, the fuzzy C-means algorithm with median modification helping to reduce blurred edges was implemented. After finding the region of interest, the fuzzy connectedness procedure was performed. This procedure permitted to extract the ligament structures. On the basis of the extracted posterior cruciate ligament structures, the three-dimensional visualization of this ligament was built and, with the support of experts' knowledge, an appropriate feature vector was constructed and its values assigned for normal and pathological cases. Correct results were obtained for over 88% of 97 cases.

4

Optymalizacja struktur neuronowo-rozmytych estymatorów zmiennych stanu układu dwumasowego

Kamiński M., Tran-Van T., Szabat K.

Przegląd Elektrotechniczny

|

2013

|

R. 89, nr 7

173--176

PL

W niniejszym artykule zaprezentowano zastosowanie modeli neuronowo-rozmytych w odtwarzaniu zmiennych stanu napędu elektrycznego o złożonej części mechanicznej. Istotnym zagadnieniem w procesie projektowania testowanych estymatorów jest optymalizacja ich struktury, w tym celu zastosowano metodę rozmytą K-średnich. Uzyskano wysoką precyzję estymowanych sygnałów (prędkości obciążenia oraz momentu skrętnego) oraz odporność, w badanym zakresie, na zmiany wybranych parametrów napędu, a także w przypadku wprowadzania dodatkowych nieliniowości elementów sprzęgających.

EN

In this paper application of neuro-fuzzy models in state variables estimation of electrical drive with composite mechanical part is presented. Important task in design process is structure optimization, for this purpose fuzzy c-means algorithm is applied. High precision of selected signals estimation (load speed and torsional torque) is obtained. Moreover estimators are robust, in tested range, against parameter changes and introduction of additional nonlinear elements in coupling between motor and load.