A NEW METHOD FOR PREDICTING EARLY-STAGE LUNG NODULES BASED ON PSO-SVM HYBRID ALGORITHM

The aim of this article was to use the Support Vector Machine (SVM) to predict the benign and malignant solitary pulmonary nodules (SPNs) in early-stage lung cancer in order to lessen the patient’s pain and save the money. Fifty and one patient records were collected .Each record consisted of four clinical characteristics and nine morphological characteristics. The SVM classifier was built by radial basis kernel function. The penalty factor C and kernel parameter σ were optimized by comparing particle swarm optimization (PSO), grid search algorithm (GSA) and genetic algorithm (GA)and then employed to diagnose the SPNs. By comparison with a Logistic regression (LR) model, the overall results of our calculation demonstrated that the area under the receiver operator characteristic (ROC) curve for the model (0.913 ± 0.051, p\u3c0.05) was higher than the LR model. The accuracy, sensitivity and specificity in the model were 90.7%, 89.3% and 93.3% respectively. It is represented that the PSO-SVM model can be used in predicting the early-stage lung nodules

A lung cancer detection approach based on shape index and curvedness superpixel candidate selection

Orientador : Lucas Ferrari de OliveiraDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa: Curitiba, 29/08/2016Inclui referências : f. 72-76Área de concentração: Sistemas eletrônicosResumo: Câncer é uma das causas com mais mortalidade mundialmente. Câncer de pulmão é o tipo de câncer mais comum (excluíndo câncer de pele não-melanoma). Seus sintomas aparecem em estágios mais avançados, o que dificulta o seu tratamento. Para diagnosticar o paciente, a tomografia computadorizada é utilizada. Ela é composta de diversos cortes, que mapeiam uma região 3D de interesse. Apesar de fornecer muitos detalhes, por serem gerados vários cortes, a análise de exames de tomografia computadorizada se torna exaustiva, o que pode influenciar negativamente no diagnóstico feito pelo especialista. O objetivo deste trabalho é o desenvolvimento de métodos para a segmentação do pulmão e a detecção de nódulos em imagens de tomografia computadorizada do tórax. As imagens são segmentadas para separar o pulmão das outras estruturas e após, detecção de nódulos utilizando a técnicas de superpixeis são aplicadas. A técnica de Rótulamento dos Eixos teve uma média de preservação de nódulos de 93,53% e a técnica Monotone Chain Convex Hull apresentou melhores resultados com uma taxa de 97,78%. Para a detecção dos nódulos, as técnicas Felzenszwalb e SLIC são empregadas para o agrupamento de regiões de nódulos em superpixeis. Uma seleção de candidatos à nódulos baseada em shape index e curvedness é aplicada para redução do número de superpixeis. Para a classificação desses candidatos, foi utilizada a técnica de Florestas Aleatórias. A base de imagens utilizada foi a LIDC, que foi dividida em duas sub-bases: uma de desenvolvimento, composta pelos pacientes 0001 a 0600, e uma de validação, composta pelos pacientes 0601 a 1012. Na base de validação, a técnica Felzenszwalb obteve uma sensibilidade de 60,61% e 7,2 FP/exame. Palavras-chaves: Câncer de pulmão. Detecção de nódulos. Superpixel. Shape index.Abstract: Cancer is one of the causes with more mortality worldwide. Lung cancer is the most common type (excluding non-melanoma skin cancer). Its symptoms appear mostly in advanced stages, which difficult its treatment. For patient diagnostic, computer tomography (CT) is used. CT is composed of many slices, which maps a 3D region of interest. Although it provides many details, its analysis is very exhaustive, which may has negatively influence in the specialist's diagnostic. The objective of this work is the development of lung segmentation and nodule detection methods in chest CT images. These images are segmented to separate the lung region from other parts and, after that, nodule detection using superpixel methods is applied. The Axes' Labeling had a mean of nodule preservation of 93.53% and the Monotone Chain Convex Hull method presented better results, with a mean of 97.78%. For nodule detection, the Felzenszwalb and SLIC methods are employed to group nodule regions. A nodule candidate selection based on shape index and curvedness is applied for superpixel reduction. Then, classification of these candidates is realized by the Random Forest. The LIDC database was divided into two data sets: a development data set composed of the CT scans of patients 0001 to 0600, and a untouched, validation data set, composed of patients 0601 to 1012. For the validation data set, the Felzenszwalb method had a sensitivity of 60.61% and 7.2 FP/scan. Key-words: Lung cancer. Nodule detection. Superpixel. Shape index

A proposed methodology for detecting the malignant potential of pulmonary nodules in sarcoma using computed tomographic imaging and artificial intelligence-based models

The presence of lung metastases in patients with primary malignancies is an important criterion for treatment management and prognostication. Computed tomography (CT) of the chest is the preferred method to detect lung metastasis. However, CT has limited efficacy in differentiating metastatic nodules from benign nodules (e.g., granulomas due to tuberculosis) especially at early stages (<5 mm). There is also a significant subjectivity associated in making this distinction, leading to frequent CT follow-ups and additional radiation exposure along with financial and emotional burden to the patients and family. Even 18F-fluoro-deoxyglucose positron emission technology-computed tomography (18F-FDG PET-CT) is not always confirmatory for this clinical problem. While pathological biopsy is the gold standard to demonstrate malignancy, invasive sampling of small lung nodules is often not clinically feasible. Currently, there is no non-invasive imaging technique that can reliably characterize lung metastases. The lung is one of the favored sites of metastasis in sarcomas. Hence, patients with sarcomas, especially from tuberculosis prevalent developing countries, can provide an ideal platform to develop a model to differentiate lung metastases from benign nodules. To overcome the lack of optimal specificity of CT scan in detecting pulmonary metastasis, a novel artificial intelligence (AI)-based protocol is proposed utilizing a combination of radiological and clinical biomarkers to identify lung nodules and characterize it as benign or metastasis. This protocol includes a retrospective cohort of nearly 2,000–2,250 sample nodules (from at least 450 patients) for training and testing and an ambispective cohort of nearly 500 nodules (from 100 patients; 50 patients each from the retrospective and prospective cohort) for validation. Ground-truth annotation of lung nodules will be performed using an in-house-built segmentation tool. Ground-truth labeling of lung nodules (metastatic/benign) will be performed based on histopathological results or baseline and/or follow-up radiological findings along with clinical outcome of the patient. Optimal methods for data handling and statistical analysis are included to develop a robust protocol for early detection and classification of pulmonary metastasis at baseline and at follow-up and identification of associated potential clinical and radiological markers

Artificial intelligence - based ultrasound elastography for disease evaluation - a narrative review

Ultrasound elastography (USE) provides complementary information of tissue stiffness and elasticity to conventional ultrasound imaging. It is noninvasive and free of radiation, and has become a valuable tool to improve diagnostic performance with conventional ultrasound imaging. However, the diagnostic accuracy will be reduced due to high operator-dependence and intra- and inter-observer variability in visual observations of radiologists. Artificial intelligence (AI) has great potential to perform automatic medical image analysis tasks to provide a more objective, accurate and intelligent diagnosis. More recently, the enhanced diagnostic performance of AI applied to USE have been demonstrated for various disease evaluations. This review provides an overview of the basic concepts of USE and AI techniques for clinical radiologists and then introduces the applications of AI in USE imaging that focus on the following anatomical sites: liver, breast, thyroid and other organs for lesion detection and segmentation, machine learning (ML) - assisted classification and prognosis prediction. In addition, the existing challenges and future trends of AI in USE are also discussed

Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0.

COVLIAS 1.0: an automated lung segmentation was designed for COVID-19 diagnosis. It has issues related to storage space and speed. This study shows that COVLIAS 2.0 uses pruned AI (PAI) networks for improving both storage and speed, wiliest high performance on lung segmentation and lesion localization.ology: The proposed study uses multicenter ∼9,000 CT slices from two different nations, namely, CroMed from Croatia (80 patients, experimental data), and NovMed from Italy (72 patients, validation data). We hypothesize that by using pruning and evolutionary optimization algorithms, the size of the AI models can be reduced significantly, ensuring optimal performance. Eight different pruning techniques (i) differential evolution (DE), (ii) genetic algorithm (GA), (iii) particle swarm optimization algorithm (PSO), and (iv) whale optimization algorithm (WO) in two deep learning frameworks (i) Fully connected network (FCN) and (ii) SegNet were designed. COVLIAS 2.0 was validated using "Unseen NovMed" and benchmarked against MedSeg. Statistical tests for stability and reliability were also conducted.Pruning algorithms (i) FCN-DE, (ii) FCN-GA, (iii) FCN-PSO, and (iv) FCN-WO showed improvement in storage by 92.4%, 95.3%, 98.7%, and 99.8% respectively when compared against solo FCN, and (v) SegNet-DE, (vi) SegNet-GA, (vii) SegNet-PSO, and (viii) SegNet-WO showed improvement by 97.1%, 97.9%, 98.8%, and 99.2% respectively when compared against solo SegNet. AUC > 0.94 (p 0.86 (p < 0.0001) on NovMed data set for all eight EA model. PAI <0.25 s per image. DenseNet-121-based Grad-CAM heatmaps showed validation on glass ground opacity lesions.Eight PAI networks that were successfully validated are five times faster, storage efficient, and could be used in clinical settings

Novel Computer-Aided Diagnosis Schemes for Radiological Image Analysis

The computer-aided diagnosis (CAD) scheme is a powerful tool in assisting clinicians (e.g., radiologists) to interpret medical images more accurately and efficiently. In developing high-performing CAD schemes, classic machine learning (ML) and deep learning (DL) algorithms play an essential role because of their advantages in capturing meaningful patterns that are important for disease (e.g., cancer) diagnosis and prognosis from complex datasets. This dissertation, organized into four studies, investigates the feasibility of developing several novel ML-based and DL-based CAD schemes for different cancer research purposes. The first study aims to develop and test a unique radiomics-based CT image marker that can be used to detect lymph node (LN) metastasis for cervical cancer patients. A total of 1,763 radiomics features were first computed from the segmented primary cervical tumor depicted on one CT image with the maximal tumor region. Next, a principal component analysis algorithm was applied on the initial feature pool to determine an optimal feature cluster. Then, based on this optimal cluster, machine learning models (e.g., support vector machine (SVM)) were trained and optimized to generate an image marker to detect LN metastasis. The SVM based imaging marker achieved an AUC (area under the ROC curve) value of 0.841 ± 0.035. This study initially verifies the feasibility of combining CT images and the radiomics technology to develop a low-cost image marker for LN metastasis detection among cervical cancer patients. In the second study, the purpose is to develop and evaluate a unique global mammographic image feature analysis scheme to identify case malignancy for breast cancer. From the entire breast area depicted on the mammograms, 59 features were initially computed to characterize the breast tissue properties in both the spatial and frequency domain. Given that each case consists of two cranio-caudal and two medio-lateral oblique view images of left and right breasts, two feature pools were built, which contain the computed features from either two positive images of one breast or all the four images of two breasts. For each feature pool, a particle swarm optimization (PSO) method was applied to determine the optimal feature cluster followed by training an SVM classifier to generate a final score for predicting likelihood of the case being malignant. The classification performances measured by AUC were 0.79±0.07 and 0.75±0.08 when applying the SVM classifiers trained using image features computed from two-view and four-view images, respectively. This study demonstrates the potential of developing a global mammographic image feature analysis-based scheme to predict case malignancy without including an arduous segmentation of breast lesions. In the third study, given that the performance of DL-based models in the medical imaging field is generally bottlenecked by a lack of sufficient labeled images, we specifically investigate the effectiveness of applying the latest transferring generative adversarial networks (GAN) technology to augment limited data for performance boost in the task of breast mass classification. This transferring GAN model was first pre-trained on a dataset of 25,000 mammogram patches (without labels). Then its generator and the discriminator were fine-tuned on a much smaller dataset containing 1024 labeled breast mass images. A supervised loss was integrated with the discriminator, such that it can be used to directly classify the benign/malignant masses. Our proposed approach improved the classification accuracy by 6.002%, when compared with the classifiers trained without traditional data augmentation. This investigation may provide a new perspective for researchers to effectively train the GAN models on a medical imaging task with only limited datasets. Like the third study, our last study also aims to alleviate DL models’ reliance on large amounts of annotations but uses a totally different approach. We propose employing a semi-supervised method, i.e., virtual adversarial training (VAT), to learn and leverage useful information underlying in unlabeled data for better classification of breast masses. Accordingly, our VAT-based models have two types of losses, namely supervised and virtual adversarial losses. The former loss acts as in supervised classification, while the latter loss works towards enhancing the model’s robustness against virtual adversarial perturbation, thus improving model generalizability. A large CNN and a small CNN were used in this investigation, and both were trained with and without the adversarial loss. When the labeled ratios were 40% and 80%, VAT-based CNNs delivered the highest classification accuracy of 0.740±0.015 and 0.760±0.015, respectively. The experimental results suggest that the VAT-based CAD scheme can effectively utilize meaningful knowledge from unlabeled data to better classify mammographic breast mass images. In summary, several innovative approaches have been investigated and evaluated in this dissertation to develop ML-based and DL-based CAD schemes for the diagnosis of cervical cancer and breast cancer. The promising results demonstrate the potential of these CAD schemes in assisting radiologists to achieve a more accurate interpretation of radiological images