Neural Network Sorting Research Articles

Analysing Breast Cancer using Convolution Neural Network

Technological development of Soft Computing and Artificial Intelligence contributed a lot towards disease diagnosis in medical science. For providing solutions to the biological inspired problems in medical domain like Breast Cancer (BC) soft computing methods can give the flexible information. As per 2020 about 2 million women were detected with Breast Cancer (BC) creating most common malignancy among women worldwide. This rises both incidence and mortality has occurred during the past three decades due to evolving risk factors, improved cancer registries, and earlier diagnosis. There is a large number of risk factors for BC, some of which can be changed and others cannot. Eighty percent of people diagnosed with BC nowadays are over the age of fifty. Molecular subtype and developmental stage are both important in determining the likelihood of survival. When it comes to clinical presentation, behaviour and shape, invasive BC span a broad spectrum of tumours. In this paper, Convolution Neural Network (CNN) used to recognize the BC tumor because it is another sort of neural network that can discover key information in both image and time series data. By applying CNN on the 2023 RSNA (Radiological Society for North America) Screening Mammography Breast Cancer data we analysed how best CNN algorithm is for identifying breast cancer with accuracy and also tried to analyse at what age Breast Cancer is mostly occurred in women.

IMPROVEMENT IN KERATOCONUS DIAGNOSIS USING MORPHO-GEOMETRIC VARIABLES WITH RNN NETWORKS

There is an eye disease called Keratoconus (KC) which has potential to cause visual acuity loss; hence, it can be considered as disability due to its severity. There are some limitations in current method in detecting cornea region’s boarder edge. Primary objective for the paper need to identify the structural description of disease’ asymmetry with the help of Morpho-geometric parameters relates with the keratoconous eyes along by means of slight visual control. It also includes the application of Recurrent Neural Network (RNN) analysis which is sort of Neural Network in which previous step’s output are sent to present step as an input. In order to determine most prominent correlation, Stepwise Discriminant Function Analysis is used in analyzing output. The Prominent correlation was identified between thinnest point in the anterior deviation and thinnest point in the posterior deviations of minor keratoconic cases. MATLAB R2014 software is used to implement the framework and analyses of simulation results were performed.

Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade.

To evaluate the performance of quantitative computed tomography (CT) texture analysis using different machine learning (ML) classifiers for discriminating low and high nuclear grade clear cell renal cell carcinomas (cc-RCCs). This retrospective study included 53 patients with pathologically proven 54 cc-RCCs (31 low-grade [grade 1 or 2]; 23 high-grade [grade 3 or 4]). In one patient, two synchronous cc-RCCs were included in the analysis. Mean age was 57.5 years. Thirty-four (64.1%) patients were male and 19 were female (35.9%). Mean tumour size based on the maximum diameter was 57.4 mm (range, 16-145 mm). Forty patients underwent radical nephrectomy and 13 underwent partial nephrectomy. Following pre-processing steps, two-dimensional CT texture features were extracted using portal-phase contrast-enhanced CT. Reproducibility of texture features was assessed with the intra-class correlation coefficient (ICC). Nested cross-validation with a wrapper-based algorithm was used in feature selection and model optimisation. The ML classifiers were support vector machine (SVM), multilayer perceptron (MLP, a sort of neural network), naïve Bayes, k-nearest neighbours, and random forest. The performance of the classifiers was compared by certain metrics. Among 279 texture features, 241 features with an ICC equal to or higher than 0.80 (excellent reproducibility) were included in the further feature selection process. The best model was created using SVM. The selected subset of features for SVM included five co-occurrence matrix (ICC range, 0.885-0.998), three run-length matrix (ICC range, 0.889-0.992), one gradient (ICC = 0.998), and four Haar wavelet features (ICC range, 0.941-0.997). The overall accuracy, sensitivity (for detecting high-grade cc-RCCs), specificity (for detecting high-grade cc-RCCs), and overall area under the curve of the best model were 85.1%, 91.3%, 80.6%, and 0.860, respectively. The ML-based CT texture analysis can be a useful and promising non-invasive method for prediction of low and high Fuhrman nuclear grade cc-RCCs. • Based on the percutaneous biopsy literature, ML-based CT texture analysis has a comparable predictive performance with percutaneous biopsy. • Highest predictive performance was obtained with use of the SVM. • SVM correctly classified 85.1% of cc-RCCs in terms of nuclear grade, with an AUC of 0.860.

A Study on Kohenen Network based on Path Determination for Efficient Moving Trajectory on Mobile Robot

We propose an approach to estimate the real-time moving trajectory of an object in this paper. The object's position is obtained from the image data of a CCD camera, while a state estimator predicts the linear and angular velocities of the moving object. To overcome the uncertainties and noises residing in the input data, a Extended Kalman Filter(EKF) and neural networks are utilized cooperatively. Since the EKF needs to approximate a nonlinear system into a linear model in order to estimate the states, there still exist errors as well as uncertainties. To resolve this problem, in this approach the Kohonen networks, which have a high adaptability to the memory of the inputoutput relationship, are utilized for the nonlinear region. In addition to this, the Kohonen network, as a sort of neural network, can effectively adapt to the dynamic variations and become robust against noises. This approach is derived from the observation that the Kohonen network is a type of self-organized map and is spatially oriented, which makes it suitable for determining the trajectories of moving objects. The superiority of the proposed algorithm compared with the EKF is demonstrated through real experiments.

Trajectory estimation of a moving object using Kalman filter and Kohonen networks

SUMMARYA novel approach to estimate the real-time moving trajectory of an object is proposed in this paper. The object's position is obtained from the image data of a charge coupled device (CCD) camera, while a state estimator predicts the linear and angular velocities of the moving object. To overcome the uncertainties and noises residing in the input data, a Kalman filter and neural networks are utilized cooperatively. Since the Kalman filter needs to approximate a nonlinear system into a linear model in order to estimate the states, there still exist errors as well as uncertainties. To resolve this problem, in this approach, the Kohonen networks, which have a high adaptability to the memory of the input–output relationship, are utilized for the nonlinear region. In addition to this, the Kohonen network, as a sort of neural network, can effectively adapt to the dynamic variations and become robust against noises. This approach is derived from the observation that the Kohonen network is a type of a self-organized map and is spatially oriented, which makes it suitable for determining the trajectories of moving objects. The superiority of the proposed algorithm compared with the extended Kalman filter is demonstrated through real experiments.