Performance of Image Processing Algorithm for Lung Cancer Classification

Lung cancer is a dangerous disease that taking human life rapidly worldwide. The death of the people is increasing exponentially because of lung cancer. In order to reduce the disease and save a human's life, the automated system is needed. The purpose of the lung cancer detection system is able to detect and provide reliable information to doctors and clinicians from the medical image. To minimize this problem, many systems have been proposed by using different image processing techniques, machine learning, and deep learning techniques. A computed tomography (CT) imaging modality is an efficient technique for medical screening used for lung cancer detection and diagnosis. Physician and radiologist use the CT scan images to analyze, interpret and diagnose the lung cancer from lung tissues. However, in most cases, obtaining an accurate diagnosis result without using the extra medical tool known as a computer- Aid detection and Diagnosis (CAD) system is tedious work for many physicians. To obtain an accurate result from computer-aided diagnosis system lung segmentation methods are basic once. So in this project, we have used different lung segmentation and nodules segmentation methods. Our work has consisted of preprocessing, and lung segmentation by using thresholding, and also used the U-net model for detection of the candidate nodules of the patient’s lung CT scan and classification methodology. We have used a convolutional neural network and designed a 3D CNN model that has a 0.77% accuracy performance.

A survey and taxonomy of 2.5D approaches for lung segmentation and nodule detection in CT images

Lung Cancer in today’s world is one of the major widespread dangerous diseases which is the subject of maximum deaths every year. Accurate detection of lung cancer could boost the endurance rate. Medical image processing has a significant impact on the recognition of lung tumors using Computer Tomography (CT) scan images. Images from a CT scan are widely used because they provide comprehensive imaging of tumor progression inside the lungs. Although different types of noise might be experienced while doing CT scans, producing it a monotonous task for recognizing tumors in the lung. Elimination of noise in CT images is a challenging task for medical diagnoses. The presence of noise in an image is inevitable. Hence reducing noise from the CT scan image is critical for further analysis. Hence various filtering techniques have been used that denoise and enhance the image and help in further evaluation of CT images for accurate lung cancer detection. This paper analyses the noises of different kinds in the CT images and different noise removal techniques which help in improving the accuracy of segmentation and feature extraction as they remove unwanted noise and contribute to the accurate detection of lung cancer. The various filtering methods are analyzed with salt along with pepper noise and speckle noise. The performances of different filters are computed in terms of metrics for evaluation like PSNR, SSIM, MSE, and SNR. The experimental results show that the median filter is more efficient in comparison to other filtering methods in eliminating noises that exist in lung CT images by owning fewer MSE values of 214.8522, high SNR value of 19.36304, SSIM value 0.595997, and high PSNR value of 24.80941.

Cancer has become very common in this evolving world. Technology advancements, increased radiations have made cancer a common syndrome. Various types of cancers like Skin Cancer, Breast Cancer, Prostate Cancer, Blood Cancer, Colorectal cancer, Kidney Cancer and Lung Cancer exits. Among these various types of cancers, the mortality rate is high in lung cancer which is tough to diagnose and can be diagnosed only in advanced stages. Small cell lung cancer and non-small cell lung cancer are the two types in which non-small cell lung cancer (NSCLC) is the most common type which makes up to 80 to 85 percent of all cases [1]. Digital Image Processing and Artificial Intelligence advancements has helped a lot in medical image analysis and Computer Aided Diagnosis(CAD). Numerous research is carried out in this field to improve the detection and prediction of the cancerous tissues. In current methods, traditional image processing techniques is applied for image processing, noise removal and feature extraction. There are few good approaches that applies Artificial Intelligence and produce better results. However, no research has achieved 100% accuracy in nodule detection, early detection of cancerous nodules nor faster processing methods. Application of Artificial Intelligence techniques like Machine Learning, Deep Learning is very minimal and limited. In this paper [Figure 1], we have applied Artificial intelligence techniques to process CT (Computed Tomography) Scan image for data collection and data model training. The DICOM image data is saved as numpy file with all medical information extracted from the files for training. With the trained data we apply deep learning for noise removal and feature extraction. We can process huge volume of medical images for data collection, image processing, detection and prediction of nodules. The patient is made well aware of the disease and enabled with their health tracking using various mobile applications made available in the online stores for iOS and Android mobile devices.

The purpose of this study was to compare the diagnostic value of 11C-choline positron emission tomography (PET) and [18F]fluorodeoxyglucose (FDG) PET imaging in the detection of primary lung cancer and mediastinal lymph node metastases. Seventeen patients with histologically proven primary lung cancer were examined with both 11C-choline and FDG PET within a week of each study. Lung cancers were analysed visually and semiquantitatively using the ratio of tumour-to-normal radioactivity (T/N ratio) and standardized uptake value (SUV). Mediastinal lymph node metastases were analysed visually. Although both techniques delineated focal lesions with an increase in tracer accumulation in 13 patients, FDG PET identified three additional patients in whom 11C-choline PET did not visualize any lesion. In the detection of lung cancer <2 cm in size, FDG PET provided higher sensitivity (six of seven, 85.7%) than 11C-choline PET (four of seven, 57.1%). The T/N ratio and SUV were significantly higher with FDG PET (T/N ratio, 7.43+/-6.22; SUV, 4.05+/-3.05) than these were with 11C-choline PET (T/N ratio, 2.93+/-1.19; SUV, 2.93+/-0.79) (P<0.001). There was a significant positive correlation between the T/N ratios and SUVs of FDG and 11C-choline. In the assessment of mediastinal lymph node involvement, FDG PET detected lymph node metastases in two patients who were negative on 11C-choline PET, whereas both techniques could not detect tumour involvement in one patient. Both techniques have clinical value for the non-invasive detection of primary lung cancer that is 2 cm or greater in size. However, FDG PET is superior to 11C-choline PET in the detection of lung cancer that is less than 2 cm in diameter and in mediastinal lymph node metastases.

BackgroundCurrently, no satisfactory biomarkers are available to screen for lung cancer. Surface-Enhanced Laser Desorption/ionization Time-of- Flight Mass Spectrometry ProteinChip system (SELDI-TOF-MS) is one of the currently used techniques to identify biomarkers for cancers. The aim of this study is to explore the application of serum SELDI proteomic patterns to distinguish lung cancer patients from healthy individuals.MethodsA total of 208 serum samples, including 158 lung cancer patients and 50 healthy individuals, were randomly divided into a training set (including 11 sera from patients with stages I/II lung cancer, 63 from patients with stages III/IV lung cancer and 20 from healthy controls) and a blinded test set (including 43 sera from patients with stages I/II lung cancer, 41 from patients with stages III/IV lung cancer and 30 from healthy controls). All samples were analyzed by SELDI technology. The spectra were generated on weak cation exchange (WCX2) chips, and protein peaks clustering and classification analyses were made using Ciphergen Biomarker Wizard and Biomarker Pattern software, respectively. We additionally determined Cyfra21-1 and NSE in the 208 serum samples included in this study using an electrochemiluminescent immunoassay.ResultsFive protein peaks at 11493, 6429, 8245, 5335 and 2538 Da were automatically chosen as a biomarker pattern in the training set. When the SELDI marker pattern was tested with the blinded test set, it yielded a sensitivity of 86.9%, a specificity of 80.0% and a positive predictive value of 92.4%. The sensitivities provided by Cyfra21-1 and NSE used individually or in combination were significantly lower than that of the SELDI marker pattern (P < 0.005 or 0.05, respectively). Based on the results of the test set, we found that the SELDI marker pattern showed a sensitivity of 91.4% in the detection of non-small cell lung cancers (NSCLC), which was significantly higher than that in the detection of small cell lung cancers (P < 0.05); The pattern also had a sensitivity of 79.1% in the detection of lung cancers in stages I/II.ConclusionThese results suggest that serum SELDI protein profiling can distinguish lung cancer patients, especially NSCLC patients, from normal subjects with relatively high sensitivity and specificity, and the SELDI-TOF-MS is a potential tool for the screening of lung cancer.

Computer Aided Diagnosis (CAD) systems for automatic detection of pulmonary diseases and lung cancer mainly depend on the segmentation of different pulmonary components like right and left lung lobes, airways, vessels, and nodules from the medical imaging modalities like CTs, MRIs, etc. Lung segmentation and nodule segmentation are the important steps to detect any lung related abnormalities. It requires many image processing operations to be performed on the medical images. Computed Tomography (CT) imaging is the most preferred modal because of its popularity, ease of use, and capability of showing different anatomical structures of thorax region. This review paper includes a study of various state of the art techniques explaining the methods applied on CT scans to find the ROIs along with their segmentation accuracies parameters in terms of similarity coefficient, mean error, and overlap ratio.

Lung cancer is a serious type of cancer, leading to increased mortality to death in both men and women as the symptoms are noticed only at later stages. Life span of individuals' may be extended if lung cancer is detected in its early stages. One of the imaging modalities used to diagnose lung cancer is computed tomography (CT). A nodule or mass is a small abnormal growth observed in a lung CT scan that, in most cases, may turn out to be benign. A computer‐aided system is essential to help physicians in precisely diagnosing the disease. The main objective of this work is to detect and classify the nodules in the lung CT scan images as benign or malignant. A context‐aware attention UNET architecture is proposed to segment the nodule from the lung CT scan image. Further, the segmented nodule is classified as benign or malignant using a Convolutional Neural Network architecture. The experiments are performed using the LUNA 16 and LIDC‐IDRI lung CT scan image datasets. From the results obtained, it is observed that the context‐aware attention UNET shows a noteworthy improvement in the following metrics: Dice Score, Sensitivity, Specificity, and F‐Measure. A significant improvement is obtained compared to the existing systems in detecting the lesion as a benign nodule or malignant nodule. Further, an ablation study is performed to validate the significance of each component in the architecture. The experimental results have reported 98.81% and 99.15% for specificity and sensitivity, respectively, and therefore the proposed system has potential clinical value in the detection of lung cancer.

The incidence and mortality rates of lung cancer have been increasing in many countries. However, the true effectiveness of screening for lung cancer is still controversial. This study aimed to examine the growth pattern of lung cancer and to evaluate the efficacy of screening. The authors linked the records of annual radiologic screening to cancer registry data and conducted a retrospective follow-up study of radiographs in all patients with lung cancer arising in a population undergoing screening. Among a total of 305,934 participants, screening detected 206 lung cancers, 103 of which were Stage I disease. Seventy-one of the 131 adenocarcinomas were Stage I, and 58% of them showed evidence of cancer for 2 years on a retrospective review of radiographs. Presentation as small faint lesions overlapping the normal chest structures delayed the early detection of adenocarcinoma. The overall sensitivity of screening was 70%, 52% for squamous cell carcinoma and 50% for small cell carcinoma. Rapidly growing Stage II-IV tumors without retrospective evidence of cancer on previous radiographs accounted for most of the cancers detected during the intervals between screening. Both the low detectability of Stage I adenocarcinoma and the late recognition of rapidly growing small cell and squamous cell carcinomas reduced the effectiveness of screening. More effective imaging methods and an antismoking campaign are required to reduce lung cancer mortality.

In this paper, we implement and analyze the image processing method for detection of lung cancer. Image processing techniques are widely used in several medical problems for picture enhancement in the detection phase to support the early medical treatment. In this research we proposed a detection method of lung cancer based on image segmentation. Image segmentation is one of intermediate level in image processing. Marker control watershed and region growing approach are used to segment of CT scan image. Detection phases are followed by image enhancement using Gabor filter, image segmentation, and features extraction. From the experimental results, we found the effectiveness of our approach. The results show that the best approach for main features detection is watershed with masking method which has high accuracy and robust.

This project constructs and assesses an image processing approach for lung cancer diagnosis in this study. Image processing techniques are frequently utilized for picture improvement in the detection phase to enable early medical therapy in a variety of medical issues. We suggested a lung cancer detection approach based on picture segmentation in this study. Image segmentation is a level of image processing that is intermediate. To segment a CT scan image, a marker control watershed and region growth technique is applied. Following the detection phases, picture augmentation with the Gabor filter, image segmentation, and feature extraction is performed. We discovered the efficiency of our strategy based on the experimental results. The results demonstrate that the watershed with the masking method, which has great accuracy and robustness, is the best strategy for detecting major features. Keywords: Lung cancer, MATLAB, CT images, Distortion removal, Segmentation, Mortality rate.

Lung cancer is one of the dangerous and life-threatening cancer diseases in the world. The most common ways to detect lung cancer is by using the Computed Tomography (CT) image. Nowadays, Computed Aided Diagnosis (CAD) is becoming more prominent. In medical applications, the CAD system is adopted to help doctors to perform an image analysis and make their final decisions. Therefore, the main aim of this research is to establish an image processing method for the segmentation of lung cancer from CT scan images. In order to achieve the main aims, the work is divided into two parts, the first is obtaining the lung region from CT scan images and the second is detecting the lesion of lung cancer. This paper will present the outcome of the first part. Firstly, the image will undergo the threshold, clustering and image filtering as well as the enhancement process to get better and clearer lung area images. Next, is the most important stage in this research which is the segmentation stage. In this work, modified watershed is used to demarcate the lung region from the CT scan images. Then, the performance of the segmentation process is measured using accuracy, recall, precision and F- score parameters. The outcome of this research is very helpful for the doctor to determine later the type of treatment that should be provided to the patient.

Computer Aided Detection (CAD) systems provide a second opinion to radiologists in detecting lung cancer by providing automated analysis of the scans. The proposed CAD system consists of five processing steps: image acquisition, preprocessing, lung segmentation, nodule detection and false positive reduction. First, 400 CT scans are downloaded from the Lung Image Database Consortium (LIDC). Preprocessing is implemented using contrast stretching and enhancing. Lung segmentation and nodule detection stages are performed using a combination of region growing, thresholding and morphological operations. Each 3D structure is then subjected to tabular structure elimination to provide nodule candidates. In the false positive reduction stage, some of the basic nodule features are extracted from the training data to set thresholds for a simple rule-based classifier. The CAD achieved sensitivity of 77.77%, specificity of 69.5% and accuracy 70.53 % with an average 4.1 FPs/scan.

Lung cancer is a deadly cancer that causes millions of deaths every year around the world. Accurate lung nodule detection and segmentation in computed tomography (CT) images is a vital step for diagnosing lung cancer early. Most existing systems face several challenges, such as the heterogeneity in CT images and variation in nodule size, shape, and location, which limit their accuracy. In an attempt to handle these challenges, this article proposes a fully automated deep learning framework that consists of lung nodule detection and segmentation models. Our proposed system comprises two cascaded stages: (1) nodule detection based on fine-tuned Faster R-CNN to localize the nodules in CT images, and (2) nodule segmentation based on the U-Net architecture with two effective blocks, namely position attention-aware weight excitation (PAWE) and channel attention-aware weight excitation (CAWE), to enhance the ability to discriminate between nodule and non-nodule feature representations. The experimental results demonstrate that the proposed system yields a Dice score of 89.79% and 90.35%, and an intersection over union (IoU) of 82.34% and 83.21% on the publicly available LUNA16 and LIDC-IDRI datasets, respectively.

This paper presents a comprehensive deep learning framework for automated lung cancer detection using VGG16 architecture with transfer learning capabilities. Lung cancer remains one of the leading causes of cancer-related mortality worldwide, with early detection being crucial for patient survival. Traditional diagnostic methods are time-consuming, invasive, and prone to human error. Our proposed system addresses these challenges by implementing a VGG16-based convolutional neural network that leverages pre-trained weights and transfer learning to classify lung images into cancerous and noncancerous categories. The methodology incorporates systematic preprocessing including image normalization, resizing, augmentation, and advanced filtering techniques using OpenCV and PIL libraries. The system integrates image processing algorithms for nodule detection, segmentation, and feature extraction. Performance evaluation demonstrates 95.2% accuracy in lung cancer classification with 94% sensitivity and 93% specificity. The Flask-based web application provides real-time image analysis capabilities with MySQL database integration for patient data management. Comparative analysis with traditional machine learning approaches shows superior performance in terms of accuracy, precision, and recall metrics. The system successfully bridges the gap between artificial intelligence and clinical practice by providing radiologists with an efficient, non-invasive diagnostic support tool that can significantly improve early lung cancer detection rates