Lung Nodule Classification on Computed Tomography Using Neural Networks

2020 ◽  
Vol 17 (8) ◽  
pp. 3427-3431
Author(s):  
A. Sivasangari ◽  
D. Deepa ◽  
L. Lakshmanan ◽  
A. Jesudoss ◽  
M. S. Roobini
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Lung Nodule ◽  
Ct Scanning ◽  
Detection Algorithm ◽  
Lung Cancer Diagnosis ◽  
Long Time ◽  
Neural Network Algorithm ◽  
Manual Processing ◽  
Performance Results

Lung cancer is a leading health issue and the major cause of death among all types of cancers. CT scanning is the popular method for lung cancer diagnosis detection. Manual processing of tomograms take long time for diagnosis. It is not an easy task. This complex work can also reduce the quality of diagnosis. Machine learning and neural network algorithm can be used to automatically process X-ray pictures, tomograms and PET images to detect diseases. The goal of the proposed work is to find any abnormal thing in lungs. Convolutional neural network is trained to classify abnormal area from the normal cells. The detection algorithm is designed to determine the existence of cancer in tomography images and validation, training and testing using CT images. The proposed work investigates the performance of classifier by training algorithm with morphological feature extraction. The performance results shows that proposed method achieves higher accuracy than existing methods.

scholarly journals An Efficient DA-Net Architecture for Lung Nodule Segmentation

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1457
Author(s):  
Muazzam Maqsood ◽  
Sadaf Yasmin ◽  
Irfan Mehmood ◽  
Maryam Bukhari ◽  
Mucheol Kim
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Ct Scan ◽  
Cancer Diagnosis ◽  
Lung Nodule ◽  
Search Space ◽  
Lung Nodules ◽  
Suggested Model ◽  
Lung Cancer Diagnosis ◽  
Segmentation Framework

A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. This method extracts rich features by integrating compactly and densely linked rich convolutional blocks merged with Atrous convolutions blocks to broaden the view of filters without dropping loss and coverage data. We first extract the lung’s ROI images from the whole CT scan slices using standard image processing operations and k-means clustering. This reduces the search space of the model to only lungs where the nodules are present instead of the whole CT scan slice. The evaluation of the suggested model was performed through utilizing the LIDC-IDRI dataset. According to the results, we found that DA-Net showed good performance, achieving an 81% Dice score value and 71.6% IOU score.

scholarly journals KeratoDetect: Keratoconus Detection Algorithm Using Convolutional Neural Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Alexandru Lavric ◽  
Popa Valentin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Treatment Options ◽  
Diagnostic Errors ◽  
Detection Algorithm ◽  
Rapid Screening ◽  
Test Set ◽  
Corneal Deformation ◽  
Long Time ◽  
High Level

Keratoconus (KTC) is a noninflammatory disorder characterized by progressive thinning, corneal deformation, and scarring of the cornea. The pathological mechanisms of this condition have been investigated for a long time. In recent years, this disease has come to the attention of many research centers because the number of people diagnosed with keratoconus is on the rise. In this context, solutions that facilitate both the diagnostic and treatment options are quickly needed. The main contribution of this paper is the implementation of an algorithm that is able to determine whether an eye is affected or not by keratoconus. The KeratoDetect algorithm analyzes the corneal topography of the eye using a convolutional neural network (CNN) that is able to extract and learn the features of a keratoconus eye. The results show that the KeratoDetect algorithm ensures a high level of performance, obtaining an accuracy of 99.33% on the data test set. KeratoDetect can assist the ophthalmologist in rapid screening of its patients, thus reducing diagnostic errors and facilitating treatment.

Lung Cancer Classification Techniques

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Prarthana K R ◽  
Bhavani K
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Imaging Techniques ◽  
Research Work ◽  
Lung Nodule ◽  
Classification Systems ◽  
Machine Learning Techniques ◽  
Ct Scans ◽  
Ann Model ◽  
Cancer Dataset

Diagnosis of lung cancer with high accuracy rate is most difficult task to make remarkable vary in survival rate of patients. Different imaging techniques are used by radiologists and specialists to diagnose lung cancer such as Computer tomography (CT), X-ray and Magnetic Resonance Imaging (MRI). These methods help us to predict the malignant or benign or normal nodules present in the lungs. This proposed work is to build a lung classification system that can classify the images as malignant or benign or normal cases and give best accuracy for predicting lung cancer. In this “IQ_OTH/NCCD” lung cancer dataset is used which consist of total 1190 images of lung CT scans slices for 110 cases. CT scans in DICOM formats is utilized in this research work. In this proposed work by applying machine learning techniques such as Artificial Neural Network (ANN) and Convolutional Neural Network (CNN), classify the malignant or normal or benign lung nodule cases and finally compare all the attained results. This work finds the accuracy of applied classification systems and finally CNN model outperforms with an accuracy of 98%. Accuracy of ANN model is observed to be 71%.

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