Early Detection of Lung Cancer Using Wavelet Feature Descriptor and Feed Forward Back Propagation Neural Networks Classifier

Nazir

Microscopy Res & Technique

et al. 2019

The emergence of cloud infrastructure has the potential to provide significant benefits in a variety of areas in the medical imaging field. The driving force behind the extensive use of cloud infrastructure for medical image processing is the exponential increase in the size of computed tomography (CT) and magnetic resonance imaging (MRI) data. The size of a single CT/MRI image has increased manifold since the inception of these imagery techniques. This demand for the introduction of effective and efficient frameworks for extracting relevant and most suitable information (features) from these sizeable images. As early detection of lungs cancer can significantly increase the chances of survival of a lung scanner patient, an effective and efficient nodule detection system can play a vital role. In this article, we have proposed a novel classification framework for lungs nodule classification with less false positive rates (FPRs), high accuracy, sensitivity rate, less computationally expensive and uses a small set of features while preserving edge and texture information. The proposed framework comprises multiple phases that include image contrast enhancement, segmentation, feature extraction, followed by an employment of these features for training and testing of a selected classifier. Image preprocessing and feature selection being the primary steps—playing their vital role in achieving improved classification accuracy. We have empirically tested the efficacy of our technique by utilizing the well‐known Lungs Image Consortium Database dataset. The results prove that the technique is highly effective for reducing FPRs with an impressive sensitivity rate of 97.45%.

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Lungs nodule detection framework from computed tomography images using support vector machine

Nazir

Microscopy Res & Technique

et al. 2019

Microscopy Res & Technique

“…Lee, Kouzani, and Hu () exhibited a sliding window method for nodule image classification and reported 100% accuracy with 1.4 false positives per test on 20 slices. Arulmurugan and Anandakumar () employed ANN classifier by using training functions (Traingd, Traingda, Traingdm, and Traingdx) and standard feed forward and back propagation learning algorithms with an accuracy of 92.6%, specificity of 100%, and sensitivity of 91.2% and a mean square error of 0.978. Wei et al () proposed an unsupervised spectral clustering approach to distinguish between benign and malignant nodules.…”

Section: Related Workmentioning

confidence: 99%

Automated lung nodule detection and classification based on multiple classifiers voting

Saba

2019

Lung cancer is the most common cause of cancer‐related death globally. Currently, lung nodule detection and classification are performed by radiologist‐assisted computer‐aided diagnosis systems. However, emerged artificially intelligent techniques such as neural network, support vector machine, and HMM have improved the detection and classification process of cancer in any part of the human body. Such automated methods and their possible combinations could be used to assist radiologists at early detection of lung nodules that could reduce treatment cost, death rate. Literature reveals that classification based on voting of classifiers exhibited better performance in the detection and classification process. Accordingly, this article presents an automated approach for lung nodule detection and classification that consists of multiple steps including lesion enhancement, segmentation, and features extraction from each candidate's lesion. Moreover, multiple classifiers logistic regression, multilayer perceptron, and voted perceptron are tested for the lung nodule classification using k‐fold cross‐validation process. The proposed approach is evaluated on the publically available Lung Image Database Consortium benchmark data set. Based on the performance evaluation, it is observed that the proposed method performed better in the stateof the art and achieved an overall accuracy rate of 100%.

“…The results of classiication accuracy were 91.1%. The author Arulmurugan et al (2018), In this paper for the segmentation method they have used region of interest which calculates from slices of the lung images. Wavelet feature is extracted from calculating from the GLCM method.…”

Section: Related Workmentioning

confidence: 99%

Diagnosis of lung carcinoma using computed tomography images

Sagar

Prabhu

2019

ijrps

Lung carcinoma disease is a serious leading death every day. Early recognition and treatment can save cancer affected the human’s life. Lung carcinoma is the most hazardous and dangerous disease. Computed Tomography scanning images are the most commonly using scanning technology in the health care industry, Computed Tomography scan images are used by doctors to analyze and identify the cancerous lesion present in the lung CT scanned images. The CAD system is a powerful tool for the radiologist to diagnosis the lesion cells in an accurate way. Computer-aided techniques and machine learning techniques use digital image processing methods to implement CAD systems. The prime aim of this investigation and experimentation is to analyze the different CAD techniques, recognizing the best-developed methods and collecting their disadvantages, faults. Analyzing the drawbacks from the previously proposed model, we are implementing new techniques to overcome the drawbacks analyzed with best accuracy results. For developed methods, the feasibility study is carried on each and every step, improvements can be made were listed out. The analysis shows that having a few low accuracies, and some have high accuracy but not having 100% accuracy. In this research, the K-means algorithm is used to segment the images. K-means algorithm is unsupervised learning, which is used when we have an unlabeled dataset, the project aims to increase the accuracy of up to 100%.