In recent years the image processing techniques are used commonly in various medical areas for improving prior detection and treatment stages, in which the time span or elapse is very important to identify the disease in the patient as possible as fast, especially in many tumours such as the lung cancer, breast cancer. This system first segments the region of interest (lung) and then analyses the separately obtained area for nodule detection in order to examine the disease. Even with several lung tumour segmentations have been presented, enhancing tumour segmentation methods are still interesting because lung tumour CT images has some complex characteristics, such as large variation in tumour appearance and uncertain tumour boundaries. To address this problem, tumour segmentation method for CT Images which takes apart non-enhancing lung tumours from healthy tissues has been carried out by clustering method. The proposed method uses pre-processing technique that remove unwanted artifacts using median and wiener filters. Initially, the segmentation of the CT images has been carried out by using K- Means clustering method. To the clustered result, EK-Mean clustering is applied . Further the features like entrpy, Contrast, Correlation,Homogenity and the area are extracted from the tumorous part of Fuzzy Ek- Means segmented Image. For feature extraction, statistic method called Gray Level Co-occurrence Matrix (GLCM). Classification is done by using the supervised neural network called the Back Propagation Network (BPN). Results of the classification gives, whether the CT Image is a normal Image or cancerous.

Lung tumour detection and classification using EK-Mean clustering

Diabetes is one of the major health problems as it causes physical disability and even death in people. Therefore, to diagnose this dangerous disease better, methods with minimum error rate must be used. Different models of artificial neural networks have the capability to diagnose this disease with minimum error. Hence, in this paper we have used probabilistic artificial neural networks for an approach to diagnose diabetes disease type II. We took advantage of Pima Indians Diabetes dataset with 768 samples in our experiments. According to this dataset, PNN is implemented in MATLAB. Furthermore, maximizing accuracy of diagnosing the Diabetes disease type II in training and testing the Pima Indians Diabetes dataset is the performance measure in this paper. Finally, we concluded that training accuracy and testing accuracy of the proposed method is 89.56% and 81.49%, respectively

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A New Artificial Neural Networks Approach for Diagnosing Diabetes Disease Type II

Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of its nodules. Radiologists use automated tools for more precise opinion. Automated detection of the affected lung nodules is complicated because of the shape similarity among healthy and unhealthy tissues. Over the years, several expert systems have been developed that help radiologists to diagnose lung cancer effectively. In this article, we have proposed a framework to precisely detect lungs cancer to classify the benign and malignant nodules. The proposed framework is tested using the subset of the publicly available dataset, i.e., the Lung Image Database Consortium image collection (LIDC-IDRI). We applied filtering and noise removal in the pre-processing phase. Furthermore, the adaptive thresholding technique (OTSU) and the semantic segmentation are used to accurately detect the unhealthy lung nodules. Overall, 13 nodules features have extracted using principal components analysis algorithm. In addition, four optimal features are selected based on the classification performance. In the classification phase, 9 different classifiers are employed for the experimentation. Empirical analysis shows that the proposed system outperformed other techniques and provides 99.23% accuracy using a logit boost classifier.

Lung nodules detection using semantic segmentation and classification with optimal features

Diseases like cancer have been termed as chronic fatal disease because of its deadly nature. The reason why cancer is termed as fatal is cancer progresses faster, and in most of the cases, these cells are detected at an advance stage. It is found that early detection of cancer is the key to lower death rate. In this study, overviews of applying AI technology for diagnosis of three types of cancer, breast, lung and liver, have been demonstrated. Various studies are reviewed for the different types of systems which are used for early detection of cancer. Automated or computer-aided systems with AI are considered as they provide a perfect fit to process a large dataset with accuracy and efficiency in detecting cancer. Diagnosis and treatment can be carried out with the help of these systems. Breast, lung and liver cancer studies have shown that some of these systems provide accurate precision in diagnosis and thus can solve the problem if these systems are implemented. However, these systems have to face a lot of hurdles to be implemented on a large scale. Image preprocessing, data management and other technology also need enhancement to be compatible with AI and machine learning algorithms to be implemented. Considering the experimental results, this study shows there is no doubt that the AI-implemented neural networks would be the future in cancer diagnosis and treatment.

Implementation of Artificial Intelligence Techniques for Cancer Detection

Background: Transplantation is the only treatment for patients with liver failure. Since the therapy imposes high expenses to the patients and community, identification of effective factors on survival of such patients after transplantation is valuable. Objectives: The current study attempted to model the survival of patients (two years old and above) after liver transplantation using neural network and Cox Proportional Hazards (Cox PH) regression models. The event is defined as death due to complications of liver transplantation. Patients and Methods: In a historical cohort study, the clinical findings of 1168 patients who underwent liver transplant surgery (from March 2008 to march 2013) at Shiraz Namazee Hospital Organ Transplantation Center, Shiraz, Southern Iran, were used. To model the one to five years survival of such patients, Cox PH regression model accompanied by three layers feed forward artificial neural network (ANN) method were applied on data separately and their prediction accuracy was compared using the area under the receiver operating characteristic curve (ROC). Furthermore, Kaplan-Meier method was used to estimate the survival probabilities in different years. Results: The estimated survival probability of one to five years for the patients were 91%, 89%, 85%, 84%, and 83%, respectively. The areas under the ROC were 86.4% and 80.7% for ANN and Cox PH models, respectively. In addition, the accuracy of prediction rate for ANN and Cox PH methods was equally 92.73%. Conclusions: The present study detected more accurate results for ANN method compared to those of Cox PH model to analyze the survival of patients with liver transplantation. Furthermore, the order of effective factors in patients’ survival after transplantation was clinically more acceptable. The large dataset with a few missing data was the advantage of this study, the fact which makes the results more reliable.

Five Years Survival of Patients After Liver Transplantation and Its Effective Factors by Neural Network and Cox Poroportional Hazard Regression Models.

To evaluate the performance of Computer Aided Diagnosis (CAD) for Lung Cancer using artificial neural intelligence on CT scan images. Lung Cancer can be summarized by evaluating region of interest using maximum entropy and supervised learning. Lung cancer seems to be the common cause of death among people throughout the world. Early detection of lung cancer seems to be the only factor which can increase the chance of survival among people.

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Nabanita Nath Chowdhury

Papers

Lung Cancer Detection Using Artificial Neural Network & Fuzzy Clustering