A Computer Aided Diagnostic Tool for the Detection of Uterine Fibroids

2013 ◽  
Vol 2 (1) ◽  
pp. 26-38
Author(s):  
N. Sriraam ◽  
L. Vinodashri
Keyword(s):  
Neural Network ◽  
Standard Deviation ◽  
Uterine Fibroid ◽  
Diagnostic Tool ◽  
Probabilistic Neural Network ◽  
Medical Community ◽  
Diagnostic Tools ◽  
Morphological Operations ◽  
Statistical Features ◽  
Computer Aided

The integration of information technology with biomedicine has provided viable diagnostic tools to the medical community. Such computer aided procedures fastens the clinical decision process without any hurdle. Among different medical imaging modalities, Ultrasonic Imaging plays a vital role in detecting gynecological pathologies. Of importance, Uterine fibroid detection requires significant attention where symptoms such as, infertility and miscarriage can be predicted. This paper suggests an automated computer aided diagnostic tool for the detection of uterine fibroid. Gabor wavelets are applied for texture segmentation and statistical features such as mean, variance, standard deviation, skewness, kurtosis, Eigen values, GLCM contrast and energy are extracted from the user defined region of interest (ROI). The qualitative procedure is examined using the morphological operations and gray level intensity variations. Two neural network models, multilayer perceptron neural network (MLP) and probabilistic neural network (PNN) are applied to classify the normal and fibroid uterus image. It is found from the experimental computer simulation, a classification accuracy of 97.25% is obtained using combinational statistical features, mean and standard deviation with PNN classifier. It can be concluded that the proposed tool can applied as an efficient Medical Expert System for diagnosing the Ultrasonic Uterus images.

scholarly journals Cancer Diagnosis of Microscopic Biopsy Images Using a Social Spider Optimisation-Tuned Neural Network

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 11
Author(s):  
Prasanalakshmi Balaji ◽  
Kumarappan Chidambaram
Keyword(s):  
Neural Network ◽  
Performance Metrics ◽  
Disease Diagnosis ◽  
Medical Community ◽  
Diagnostic Tools ◽  
Clinical Cancer Research ◽  
Cancer Disease ◽  
Social Spider ◽  
Life Threatening ◽  
Sensitivity Specificity

One of the most dangerous diseases that threaten people is cancer. If diagnosed in earlier stages, cancer, with its life-threatening consequences, has the possibility of eradication. In addition, accuracy in prediction plays a significant role. Hence, developing a reliable model that contributes much towards the medical community in the early diagnosis of biopsy images with perfect accuracy comes to the forefront. This article aims to develop better predictive models using multivariate data and high-resolution diagnostic tools in clinical cancer research. This paper proposes the social spider optimisation (SSO) algorithm-tuned neural network to classify microscopic biopsy images of cancer. The significance of the proposed model relies on the effective tuning of the weights of the neural network classifier by the SSO algorithm. The performance of the proposed strategy is analysed with performance metrics such as accuracy, sensitivity, specificity, and MCC measures, and the attained results are 95.9181%, 94.2515%, 97.125%, and 97.68%, respectively, which shows the effectiveness of the proposed method for cancer disease diagnosis.

scholarly journals A Novel Classification of MRI Brain Images using ANFIS and 3D Reconstruction

2019 ◽  
Vol 9 (1) ◽  
pp. 5434-5440
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Brain Mri ◽  
Classification Algorithm ◽  
The Other ◽  
Morphological Operations ◽  
Detection Mechanism ◽  
Tumor Region ◽  
The Brain

The brain tumor (BT) has turn into a chief hazard to life in many humans. With the advances in medicine and technology, early tumor detection may pay a way for treating it in an early phase and thereby reducing the death rates. MRI imaging has a significant role in imaging the BT. Neurologist base the treatment of BT on the type, location, and also size of the tumor and hence proper segmentation of the tumor region has become essential. Here, an efficient segmentation algorithm is proposed, is centered on using the Sobel edge detection mechanism and the classification of the tumor region centered on the features extorted as of the segmented images are performed. The brain MRI images from a database which contain normal and also abnormal cases. These images are stripped from the skull by utilizing the morphological operations like morphological opening along with closing. Following this, segmentation is executed and finally, features are extorted as well presented to the classifier where the classification is executed. The segmentations algorithm is estimated and also the outcome are contrasted to the other prevailing algorithms say Kmeans and SVM (Support Vectors Machine) and the classification algorithm is weighted against that of the existent classification algorithm like PNN (probabilistic neural network) and ANN (Artificial Neural Network). Thus the proposed algorithms are confirmed to be superior to the other algorithms used in BT segmentations and classification.

scholarly journals Cancer Diagnosis of Microscopic Biopsy Images Using Social Spider Optimization Tuned Neural Network

2021 ◽  
Author(s):  
Prasanalakshmi Balaji ◽  
Kumarappan Chidambaram
Keyword(s):  
Neural Network ◽  
Performance Metrics ◽  
Disease Diagnosis ◽  
Medical Community ◽  
Diagnostic Tools ◽  
Clinical Cancer Research ◽  
Cancer Disease ◽  
Social Spider ◽  
Social Spider Optimization ◽  
Sensitivity Specificity

One of the most dangerous diseases that threaten people is Cancer. Cancer if diagnosed in earlier stages can be eradicated with its life threatening consequences. In addition, accuracy in prediction plays a major role. Hence, developing a reliable model that contributes much towards the medical community in early diagnosis of Biopsy images with perfect accuracy come to the scenario. The article aims towards development of better predictive models using multi-variate data and high-resolution diagnostic tools in clinical cancer research. This paper proposes the social spider optimization (SSO) algorithm tuned neural network to classify microscopic biopsy images of cancer. The significance of the proposed model relies on the effective tuning of the weights of the NN classifier by the SSO algorithm. The performance of the proposed strategy is analysed with the performance metrics, such as accuracy, sensitivity, specificity, and MCC measures, and are attained to be 95.9181%, 94.2515%, 97.125%, and 97.68% respectively, which shows the effectiveness of the proposed method in effective cancer disease diagnosis.

SEGMENTATION OF LIVER TUMOR USING FAST GREEDY SNAKE ALGORITHM

2019 ◽  
Vol 31 (02) ◽  
pp. 1950013 ◽  
Author(s):  
T. Rajalakshmi ◽  
U. Snekhalatha ◽  
Jisha Baby
Keyword(s):  
Neural Network ◽  
Standard Deviation ◽  
Liver Tumor ◽  
Liver Tumors ◽  
Ct Images ◽  
Neural Network Classifier ◽  
Statistical Features ◽  
Automated Method ◽  
Significant Difference ◽  
Liver Image

Back Ground: Liver tumors are a type of growth found in the liver which can be categorized as malignant or benign. It is also called as hepatic tumors. Early stage detection of tumor could be treated at a faster phase; if it is left undiagnosed it may lead to several complications. Traditional method adopted for diagnosis can be time consuming, error-prone and also requires an experts study. Hence a non invasive diagnostic method is required which overcomes the flaws of conventional method. Liver segmentation from CT images in post processing techniques not only is an essential prerequisite, but, by playing an important role in confirming liver function, pathological, and anatomical studies, is also a key technique for diagnosis of liver disease. Hence in the proposed study Fast greedy snakes algorithm in abdominal CT images were used for segmenting tumor portion. Aim & Objectives: The aim and objectives of study is: (i) to segment tumor region in the liver image using Fast Greedy Snakes Algorithm (FGSA); (ii) to extract the GLCM features from the segmented region; (iii) to classify the normal and abnormal liver image using neural network classifier. Methodology: The study involved a total of 30 normal and 30 abnormal Images from database. In the proposed study automated segmentation was performed using Fast Greedy Snakes (FGS) Algorithm and the features were extracted using GLCM method. Classification of normal and abnormal images was carried out using Back propagation Neural Network classifier. Result: The proposed FGS algorithm provides accurate segmentation in liver images. Statistical features like mean, kurtosis, correlation and Entropy showed a higher value for the normal image than liver tumor image. On the other hand, features like Skewness, Homogeneity, contrast, Energy and standard deviation showed a comparatively higher value for a liver tumor image than the normal. Statistical features such as Mean, Contrast, Homogeneity and standard deviation are statistically significant at [Formula: see text]. Features like correlation, entropy and energy exhibits significance at [Formula: see text]. The feature extracted values provided significant difference between the normal and abnormal liver images. The neural network classifier yields the sensitivity of 95.8%, sensitivity of 81.4% and achieved the overall accuracy of 92%. Conclusion: A most accurate, reliable and fast automated method was implemented to segment the liver tumor image using Fast Greedy snakes algorithm. Hence the proposed algorithm resulted in effective segmentation and the classifier could classify the normal and abnormal images with greater accuracy.

Classification and Identification of Power System Disturbances Using Wavalet and Artificial Neural Network Technique

2005 ◽  
Vol 2 (2) ◽  
pp. 25
Author(s):  
Noraliza Hamzah ◽  
Wan Nor Ainin Wan Abdullah ◽  
Pauziah Mohd Arsad
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Microsoft Excel ◽  
Waveform Data ◽  
The Neural Network ◽  
Harmonic Voltage ◽  
Power Quality Disturbances ◽  
Electronic Load ◽  
Wavelet Technique

Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code.  Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.

scholarly journals An efficient technique for CT scan images classification of COVID-19

2020 ◽  
pp. 1-14
Author(s):  
Esraa Hassan ◽  
Noha A. Hikal ◽  
Samir Elmuogy
Keyword(s):  
Neural Network ◽  
Diagnostic Tool ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Performance Metrics ◽  
Classification Model ◽  
Data Set ◽  
Training Models ◽  
The Earth ◽  
Diagnostic Time

Nowadays, Coronavirus (COVID-19) considered one of the most critical pandemics in the earth. This is due its ability to spread rapidly between humans as well as animals. COVID_19 expected to outbreak around the world, around 70 % of the earth population might infected with COVID-19 in the incoming years. Therefore, an accurate and efficient diagnostic tool is highly required, which the main objective of our study. Manual classification was mainly used to detect different diseases, but it took too much time in addition to the probability of human errors. Automatic image classification reduces doctors diagnostic time, which could save human’s life. We propose an automatic classification architecture based on deep neural network called Worried Deep Neural Network (WDNN) model with transfer learning. Comparative analysis reveals that the proposed WDNN model outperforms by using three pre-training models: InceptionV3, ResNet50, and VGG19 in terms of various performance metrics. Due to the shortage of COVID-19 data set, data augmentation was used to increase the number of images in the positive class, then normalization used to make all images have the same size. Experimentation is done on COVID-19 dataset collected from different cases with total 2623 where (1573 training,524 validation,524 test). Our proposed model achieved 99,046, 98,684, 99,119, 98,90 In terms of Accuracy, precision, Recall, F-score, respectively. The results are compared with both the traditional machine learning methods and those using Convolutional Neural Networks (CNNs). The results demonstrate the ability of our classification model to use as an alternative of the current diagnostic tool.

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