Lung Nodule Segmentation using Deep Learning and Advanced UNet Model

Lung cancer is one of the major causes of cancer-related deaths due to its aggressive nature and delayed detections at advanced stages. Early detection of lung cancer is very important for the survival of an individual, and is a significant challenging problem. Generally, chest radiographs (X-ray) and computed tomography (CT) scans are used initially for the diagnosis of the malignant nodules; however, the possible existence of benign nodules leads to erroneous decisions. At early stages, the benign and the malignant nodules show very close resemblance to each other. In this paper, a novel deep learning-based model with multiple strategies is proposed for the precise diagnosis of the malignant nodules. Due to the recent achievements of deep convolutional neural networks (CNN) in image analysis, we have used two deep three-dimensional (3D) customized mixed link network (CMixNet) architectures for lung nodule detection and classification, respectively. Nodule detections were performed through faster R-CNN on efficiently-learned features from CMixNet and U-Net like encoder–decoder architecture. Classification of the nodules was performed through a gradient boosting machine (GBM) on the learned features from the designed 3D CMixNet structure. To reduce false positives and misdiagnosis results due to different types of errors, the final decision was performed in connection with physiological symptoms and clinical biomarkers. With the advent of the internet of things (IoT) and electro-medical technology, wireless body area networks (WBANs) provide continuous monitoring of patients, which helps in diagnosis of chronic diseases—especially metastatic cancers. The deep learning model for nodules’ detection and classification, combined with clinical factors, helps in the reduction of misdiagnosis and false positive (FP) results in early-stage lung cancer diagnosis. The proposed system was evaluated on LIDC-IDRI datasets in the form of sensitivity (94%) and specificity (91%), and better results were obatined compared to the existing methods.

Accurate pulmonary nodule detection is a crucial step in lung cancer screening. Computer-aided detection (CAD) systems are not routinely used by radiologists for pulmonary nodule detection in clinical practice despite their potential benefits. Maximum intensity projection (MIP) images improve the detection of pulmonary nodules in radiological evaluation with computed tomography (CT) scans. Inspired by the clinical methodology of radiologists, we aim to explore the feasibility of applying MIP images to improve the effectiveness of automatic lung nodule detection using convolutional neural networks (CNNs). We propose a CNN-based approach that takes MIP images of different slab thicknesses (5 mm, 10 mm, 15 mm) and 1 mm axial section slices as input. Such an approach augments the two-dimensional (2-D) CT slice images with more representative spatial information that helps discriminate nodules from vessels through their morphologies. Our proposed method achieves sensitivity of 92.7% with 1 false positive per scan and sensitivity of 94.2% with 2 false positives per scan for lung nodule detection on 888 scans in the LIDC-IDRI dataset. The use of thick MIP images helps the detection of small pulmonary nodules (3 mm-10 mm) and results in fewer false positives. Experimental results show that utilizing MIP images can increase the sensitivity and lower the number of false positives, which demonstrates the effectiveness and significance of the proposed MIP-based CNNs framework for automatic pulmonary nodule detection in CT scans. The proposed method also shows the potential that CNNs could gain benefits for nodule detection by combining the clinical procedure.

The brain tumor is a devastating disease, which shortens the life expectancy quite a bit in their highest grade. We aim to present a multiclass brain tumor classification algorithm using deep networks convolution neural networks with loss function as the binary cross-entropy and optimizer as Adam and ResNet50 on multiple datasets of Magnetic Resonance Images. Deep learning has earned a lot of development in the medical field in the last few years. MRI has been a constructive tool for medical research in current years and has many applications like brain tumor detection. In the detection of tumors, MRI images of the brain were classified as tumorous or non-tumorous using deep network CNN. In the classification, contrast-enhanced MR images are used to classify the images into three types of brain tumor: pituitary, glioma, and meningioma. By making use of this, we can identify and give the required medication to the patients.KeywordsBrain tumorDeep learningCNNFigshareTransfer learning

Automatic detection of lung nodules in CT datasets based on stable 3D mass–spring models

ObjectiveTo evaluate the relationship between CT dose and the performance of a computer-aided diagnosis (CAD) system, and to determine how best to minimize patient exposure to ionizing radiation while maintaining sufficient image quality for automated lung nodule detection, by the use of lung cancer screening CT.Materials and MethodsTwenty-five asymptomatic volunteers participated in the study. Each volunteer underwent a low-dose CT scan without contrast enhancement (multidetector CT with 16 detector rows, 1.25 mm section thickness, 120 kVp, beam pitch 1.35, 0.6 second rotation time, with 1.25 mm thickness reconstruction at 1.25 mm intervals) using four different amperages 32, 16, 8, and 4 mAs. All series were analyzed using a commercially available CAD system for automatic lung nodule detection and the results were reviewed by a consensus reading by two radiologists. The McNemar test and Kappa analysis were used to compare differences in terms of the abilities to detect pulmonary nodules.ResultsA total of 78 non-calcified true nodules were visualized in the 25 study subjects. The sensitivities for nodule detection were as follows: 72% at 32 mAs, 64% at 16 mAs, 59% at 8 mAs, and 40% at 4 mAs. Although the overall nodule-detecting performance was best at 32 mAs, no significant difference in nodule detectability was observed between scans at 16 mAs or 8 mAs versus 32 mAs. However, scans performed at 4 mAs were significantly inferior to those performed at 32 mAs (p < 0.001).ConclusionReducing the radiation dose (i.e. reducing the amperage) lowers lung nodule detectability by CAD. However, relatively low dose scans were found to be acceptable and to cause no significant reduction in nodule detectability versus usual low-dose CT.

To evaluate the performance of a fully automated computerized method for the detection of lung nodules in computed tomographic (CT) scans in the identification of lung cancers that may be missed during visual interpretation. A database of 38 low-dose CT scans with 50 lung nodules was obtained from a lung cancer screening program. Thirty-eight of the nodules represented biopsy-confirmed lung cancers that had not been reported during initial clinical interpretation. A computer detection method that involved the use of gray-level thresholding techniques to identify three-dimensionally contiguous structures within the lungs was applied to the CT data. Computer-extracted volume was used to determine whether a structure became a nodule candidate. A rule-based scheme and a cascaded automated classifier were applied to the set of nodule candidates to distinguish actual nodules from areas of normal anatomy. Overall performance of the computer detection method was evaluated with free-response receiver operating characteristic (FROC) analysis. At a specific operating point on the FROC curve, the method achieved a sensitivity of 80% (40 of 50 nodules), with an average of 1.0 false-positive detection per section. Missed cancers were detected by the computerized method with a sensitivity of 84% (32 of 38 nodules) and a false-positive rate of 1.0 per section. With an automated lung nodule detection method, a large fraction (84%, 32 of 38) of missed cancers in a database of low-dose CT scans were detected correctly.

Lung cancer, one of the most lethal malignancies globally, has a high mortality rate, making early screening and accurate diagnosis crucial for improving patient survival rates. Low-dose computed tomography (LDCT) has significantly increased the detection rate of lung nodules, but the vast amount of imaging data poses a significant challenge for manual interpretation. This article proposed a lung nodule computer-aided quantitative diagnostic system based on a three-dimensional convolutional neural network (3D CNN), named 3D-DHA-MAFNet. By integrating deformable convolution network (DCN), hybrid attention mechanism (HAM), and multi-view adaptive fusion network (MAFN), this system significantly enhanced the detection and benign/malignant classification capabilities for small and irregular lung nodules. The model was trained and evaluated on the lung nodule analysis 2016 (LUNA16) and LIDC-IDRI datasets with a patient-level split in the ratio of 7:2:1. The number of training epochs was 200. Data augmentation methods included random rotation (±15°), translation (±5 pixels), and elastic deformation. The performance was compared with baseline models including 3D-UNet, AttentNet, 3DAGNet, 3D multi-scale capsule network (3D-MCN), and 3D multi-view SE CNN (3D MVSECNN). Experiments showed that on the LUNA16 dataset, the model achieved a detection sensitivity of 98.7% with a low false-positive rate of 1.2 per scan, which was a notable improvement over baseline models like 3D-UNet. On the LIDC-IDRI dataset, the classification accuracy for benign and malignant nodules reached 96.8%, with an AUC value of 0.983, and particularly, the sensitivity for malignant nodules was enhanced to 97.5%. Ablation studies verified the synergistic gains of each module, and cross-clinical dataset testing further demonstrated its strong generalization ability. This article presents a high-precision and interpretable intelligent diagnostic tool designed for clinical applications, which holds strong potential to promote the clinical translation of early lung cancer screening.

Tuberculosis (TB) is one of the top 10 infectious disease-related deaths. This paper uses Convolutional Neural Networks (CNN) to investigate the accuracy and performance of three pre-trained models with different optimizers and loss functions to diagnose tuberculosis based on the patient's chest X-ray scans. The odds of treating and curing tuberculosis (TB) are better if the disease is diagnosed early in a patient. Early detection of tuberculosis could lead to a decreased overall mortality rate. The best and quickest way to identify tuberculosis is to look at the patient's chest X-Ray image (CXR). A qualified professional Radiologist is required to make an accurate diagnosis. But do not have qualified doctor or radiologist everywhere. On the other hand, it is quite difficult for a doctor or radiologist to diagnose from any x-ray images with open eyes. 914 normal chest x-ray images and 892 TB infected images were used from different sources to train and evaluate these images to detect the exact x-ray of Tuberculosis infected people. Different famous pre-trained models like VGG16, InceptionV3 and Xception etc. were applied. Approximately 80% of the data was used for training and the remaining 20% was used for validation. From all of these datasets, randomly 190 images from normal and 180 images from TB chest x-ray images have been taken. Those randomized 370 (190 for TB and 180 for normal) images were used to evaluate the data finally. Performance of different algorithm like VGG16, InceptionV3 and Xception by applying different optimizers (Adam, Adadelta, Adagrad, Adamax, RMSprop, Nadam, SGD), different loss functions (Binary Cross Entropy, Hinge, Squared Hinge), varying input image size and also varying batch size were also been recorded. Note that, huge variations of performance for different combinations of algorithm, optimizer, loss function, input image size, batch size have been observed. Confusion matrix, precision, recall, f1-score value have also been recorded to understand and justify how accurately the model is predicting the disease from different angles.

To extra validate and evaluate the reproducibility of a commercial deep convolutional neural network (DCNN) algorithm for pulmonary nodules on chest radiographs (CRs) and to compare its performance with radiologists. This retrospective study enrolled 434 CRs (normal to abnormal ratio, 246:188) from 378 patients that visited a tertiary hospital. DCNN performance was compared with two radiology residents and two thoracic radiologists. Abnormality assessment (using the area under the receiver operating characteristics (AUROC)) and nodule detection (using jackknife alternative free-response ROC (JAFROC)) were compared among three groups (DCNN only, radiologist without DCNN and radiologist with DCNN). A subset of 56 paired cases, having two CRs taken within a 7-day period, were assessed for intraobserver reproducibility using the intraclass correlation coefficient. Independent characteristics of pulmonary nodules detected by DCNN were assessed by multiple logistic regression analysis. The AUROC for abnormality detection for the three groups were 0.87, 0.93 and 0.96, respectively (P<0.05), whereas the JAFROC analysis of nodule detection was 0.926, 0.929 and 0.964. Reproducibility for the three groups was 0.80, 0.67 and 0.80, which shows an increase in radiologists using DCNN (P<0.05). Nodules detected by DCNN were more solid, round-shaped and well marginated, not masked and laterally located (P<0.05). Extra validation results of DCNN showed high ROC results and there was a significant improvement in the performance when radiologists used DCNN. Reproducibility by DCNN alone showed good agreement, and there was an improvement from moderate to good agreement for radiologists using DCNN.

Introduction: Precise segmentation of the liver from computed tomography (CT) scans is an essential component in the diagnosis and treatment of liver diseases such as hepatocellular carcinoma. Manual segmentation can be time-consuming and subject to variability, spurring the application of automated systems based on deep learning. We introduce a 3D U-Net-based convolutional neural network for end-to-end segmentation of the liver from volumetric CT images. The model is trained and tested on the LiTS dataset using a standard preprocessing pipeline and an in-house composite loss function—AdvancedComboLoss—combining Dice Loss, Binary Cross-Entropy, and Focal Tversky Loss to balance class imbalance and boundary accuracy. The network handles entire 3D volumes with spatial context retained between anatomical planes and is trained on a GPU-enabled setup with the PyTorch environment. Objectives: To develop a highly accurate and computationally efficient 3D U-Net-based deep learning model for automated liver segmentation from volumetric CT scans, addressing challenges like class imbalance, anatomical variability, and low tissue contrast, using a custom loss function (AdvancedComboLoss) and a standardized preprocessing pipeline. Methods: The methodology adopted is volumetric liver segmentation from a specially designed 3D U-Net architecture specific to medical CT scans. The process entails preprocessing of raw CT scans into a common shape and intensity range and training a fully convolutional 3D neural network that learns the spatial relationships among all three anatomical planes. The model uses an encoder-decoder architecture with skip connections to maintain fine-grained structural information, allowing for accurate delineation of liver regions. A hybrid loss function—integrating Dice, Binary Cross-Entropy, and Focal Tversky losses—is used to direct the learning process to optimize overlap of regions, accuracy of classification, and class imbalance. Results: Experimental outcomes demonstrate that the model has a Dice score of 0.9604 and voxel-wise accuracy of 0.9979, indicating excellent performance in segmentation as well as strong agreement with expert annotations. The model presented in this paper presents a solid, scalable, and clinically valuable approach to automated liver segmentation with future potential to integrate into multi-stage liver and tumor analysis workflows. Conclusions: Herein, we built and tested a 3D U-Net deep learning architecture for unsupervised liver segmentation from abdominal CT volumes. Through utilization of volumetric convolutions as well as through a well-engineered composite loss function, the model was successful in capturing complicated liver anatomy as well as being able to hold high performance even on varied CT volumes. The power of the method is not just in its quantitative performance but also in its capability to generate anatomically consistent and visually plausible segmentations, making it extremely relevant to actual clinical workflows. Although there are some limitations—constrained batch size and absence of data augmentation—there is room for improvement, the findings affirm that deep learning can be an effective tool for automating key tasks in medical image analysis. As a whole, this paper adds a robust and effective segmentation model that would be potentially embedded in wider diagnostic workflows, ending up improving the accuracy, speed, and scalability of liver evaluation in clinical practice. In brief, this paper adds a high-fidelity, scalable, and computationally effective solution for computer-aided liver segmentation, pushing the application of artificial intelligence in clinical decision-making and medical image analysis.

Existing segmentation and augmentation techniques on convolutional neural network (CNN) has produced remarkable progress in object detection. However, the nominal accuracy and performance might be downturned with the photometric variation of images that are directly ignored in the training process, along with the context of the individual CNN algorithm. In this paper, we investigate the effect of a photometric variation like brightness and sharpness on different CNN. We observe that random augmentation of images weakens the performance unless the augmentation combines the weak limits of photometric variation. Our approach has been justified by the experimental result obtained from the PASCAL VOC 2007 dataset, with object detection CNN algorithms such as YOLOv3 (You Only Look Once), Faster R-CNN (Region-based CNN), and SSD (Single Shot Multibox Detector). Each CNN model shows performance loss for varying sharpness and brightness, ranging between −80% to 80%. It was further shown that compared to random augmentation, the augmented dataset with weak photometric changes delivered high performance, but the photometric augmentation range differs for each model. Concurrently, we discuss some research questions that benefit the direction of the study. The results prove the importance of adaptive augmentation for individual CNN model, subjecting towards the robustness of object detection.

Lung Cancer is one of the most dangerous threats for human life since many decades. When compared to other types of cancers, Lung cancer is very prevalent with less survival rate. Computed-Tomography-images are the main source of lung nodule detection as it provides detailed information about the nodules. Though there are many advanced techniques available in the present scenario like CNN, deep Neural Networks etc. For training purposes, all these existing methods use raw chest CT images which contain more complicated information like Blood vessels, muscles, lymph nodes, air ways, bones etc. Hence if the nodule is segmented effectively and that in turn used to train the system to classify cancerous and non-cancerous, then the results will be extraordinarily efficient. This paper presents the simple and efficient segmentation of Lung nodules and automatic classification of Nodules into Benign or Malignant using novel algorithms. The techniques used includes thresholding, morphological operations, pixels closures and mathematical model, for effective detection and classification of Nodules. The proposed algorithm is evaluated on axial plane CT Scans from LIDC-IDRI database, and the comparative study proved the effectiveness of segmentation and classification. The experimental result achieved the specificity of 98.67%, Accuracy of 97.98% and F-score of 1.0859 for β = 0.5.KeywordsComputed TomographyBenignMalignantSegmentationClassificationThresholdingMorphologyImage Processing

Convolutional neural networks (CNNs) play a crucial role in the early detection of diagnoses, assisting healthcare professionals in decision-making. This study uses different CNNs (AlexNet, ResNet-50, and EfficientNet) to classify computed tomography (CT) scan images for lung cancer detection, using the IQ-QTH/NCC and LIDC-IDRI datasets. The models were trained and evaluated with various magnifications and epochs, measuring the performance of each model based on metrics such as accuracy, precision, and recall. The results showed that ResNet-50 achieved an average accuracy of 96.12%. AlexNet reached 94.38%, and EfficientNet obtained 94.38%.

Adaptive morphology aided 2-pathway convolutional neural network for lung nodule classification

Abstract— The robust nodule detection challenge in lung cancer identification has become difficult due to the variability of lung nodules and the complexity of the surrounding environment. Early detection of lung nodules is crucial for lung cancer survival and is an effective strategy to reduce patient mortality. The proposed method for identifying lung nodules from CT images utilizes VGG-16 convolutional neural networks, eliminating the need for manual feature extraction, as per previous feedback. The network is fed with raw lung CT images from publicly available LIDC-IDRI dataset. The VGG-16 convolutional neural network successfully classified lung CT images into benign and malignant categories, achieving 86% accuracy and reducing false positive rates. Keywords— VGG-16, Lung Cancer, Computed Tomography, Classification