Lung nodules identification in CT scans using multiple instance learning.

Computer Aided Diagnosis (CAD) systems for lung nodules diagnosis aim to classify nodules into benign or malignant based on images obtained from diverse imaging modalities such as Computer Tomography (CT). Automated CAD systems are important in medical domain applications as they assist radiologists in the time-consuming and labor-intensive diagnosis process. However, most available methods require a large collection of nodules that are segmented and annotated by radiologists. This process is labor-intensive and hard to scale to very large datasets. More recently, some CAD systems that are based on deep learning have emerged. These algorithms do not require the nodules to be segmented, and radiologists need to only provide the center of mass of each nodule. The training image patches are then extracted from volumes of fixed-sized centered at the provided nodule\u27s center. However, since the size of nodules can vary significantly, one fixed size volume may not represent all nodules effectively. This thesis proposes a Multiple Instance Learning (MIL) approach to address the above limitations. In MIL, each nodule is represented by a nested sequence of volumes centered at the identified center of the nodule. We extract one feature vector from each volume. The set of features for each nodule are combined and represented by a bag. Next, we investigate and adapt some existing algorithms and develop new ones for this application. We start by applying benchmark MIL algorithms to traditional Gray Level Co-occurrence Matrix (GLCM) engineered features. Then, we design and train simple Convolutional Neural Networks (CNNs) to learn and extract features that characterize lung nodules. These extracted features are then fed to a benchmark MIL algorithm to learn a classification model. Finally, we develop new algorithms (MIL-CNN) that combine feature learning and multiple instance classification in a single network. These algorithms generalize the CNN architecture to multiple instance data. We design and report the results of three experiments applied on both generative (GLCM) and learned (CNN) features using two datasets (The Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) \cite{armato2011lung} and the National Lung Screening Trial (NLST) \cite{national2011reduced}). Two of these experiments perform five-fold cross-validations on the same dataset (NLST or LIDC). The third experiment trains the algorithms on one collection (NLST dataset) and tests it on the other (LIDC dataset). We designed our experiments to compare the different features, compare MIL versus Single Instance Learning (SIL) where a single feature vector represents a nodule, and compare our proposed end-to-end MIL approaches to existing benchmark MIL methods. We demonstrate that our proposed MIL-CNN frameworks are more accurate for the lung nodules diagnosis task. We also show that MIL representation achieves better results than SIL applied on the ground truth region of each nodule

Pneumotsütoom – kopsu ümarvarju haruldane põhjus

Eesti Arst 2016; 95(8):536–54

Machine learning approaches for lung cancer diagnosis.

The enormity of changes and development in the field of medical imaging technology is hard to fathom, as it does not just represent the technique and process of constructing visual representations of the body from inside for medical analysis and to reveal the internal structure of different organs under the skin, but also it provides a noninvasive way for diagnosis of various disease and suggest an efficient ways to treat them. While data surrounding all of our lives are stored and collected to be ready for analysis by data scientists, medical images are considered a rich source that could provide us with a huge amount of data, that could not be read easily by physicians and radiologists, with valuable information that could be used in smart ways to discover new knowledge from these vast quantities of data. Therefore, the design of computer-aided diagnostic (CAD) system, that can be approved for use in clinical practice that aid radiologists in diagnosis and detecting potential abnormalities, is of a great importance. This dissertation deals with the development of a CAD system for lung cancer diagnosis, which is the second most common cancer in men after prostate cancer and in women after breast cancer. Moreover, lung cancer is considered the leading cause of cancer death among both genders in USA. Recently, the number of lung cancer patients has increased dramatically worldwide and its early detection doubles a patient’s chance of survival. Histological examination through biopsies is considered the gold standard for final diagnosis of pulmonary nodules. Even though resection of pulmonary nodules is the ideal and most reliable way for diagnosis, there is still a lot of different methods often used just to eliminate the risks associated with the surgical procedure. Lung nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. A pulmonary nodule is the first indication to start diagnosing lung cancer. Lung nodules can be benign (normal subjects) or malignant (cancerous subjects). Large (generally defined as greater than 2 cm in diameter) malignant nodules can be easily detected with traditional CT scanning techniques. However, the diagnostic options for small indeterminate nodules are limited due to problems associated with accessing small tumors. Therefore, additional diagnostic and imaging techniques which depends on the nodules’ shape and appearance are needed. The ultimate goal of this dissertation is to develop a fast noninvasive diagnostic system that can enhance the accuracy measures of early lung cancer diagnosis based on the well-known hypotheses that malignant nodules have different shape and appearance than benign nodules, because of the high growth rate of the malignant nodules. The proposed methodologies introduces new shape and appearance features which can distinguish between benign and malignant nodules. To achieve this goal a CAD system is implemented and validated using different datasets. This CAD system uses two different types of features integrated together to be able to give a full description to the pulmonary nodule. These two types are appearance features and shape features. For the appearance features different texture appearance descriptors are developed, namely the 3D histogram of oriented gradient, 3D spherical sector isosurface histogram of oriented gradient, 3D adjusted local binary pattern, 3D resolved ambiguity local binary pattern, multi-view analytical local binary pattern, and Markov Gibbs random field. Each one of these descriptors gives a good description for the nodule texture and the level of its signal homogeneity which is a distinguishable feature between benign and malignant nodules. For the shape features multi-view peripheral sum curvature scale space, spherical harmonics expansions, and different group of fundamental geometric features are utilized to describe the nodule shape complexity. Finally, the fusion of different combinations of these features, which is based on two stages is introduced. The first stage generates a primary estimation for every descriptor. Followed by the second stage that consists of an autoencoder with a single layer augmented with a softmax classifier to provide us with the ultimate classification of the nodule. These different combinations of descriptors are combined into different frameworks that are evaluated using different datasets. The first dataset is the Lung Image Database Consortium which is a benchmark publicly available dataset for lung nodule detection and diagnosis. The second dataset is our local acquired computed tomography imaging data that has been collected from the University of Louisville hospital and the research protocol was approved by the Institutional Review Board at the University of Louisville (IRB number 10.0642). These frameworks accuracy was about 94%, which make the proposed frameworks demonstrate promise to be valuable tool for the detection of lung cancer

Mastoiditis and facial paralysis as initial manifestations of Wegener's Granulomatosis

Wegener's Granulomatosis (WG) is characterized by necrotizing granulomas and vasculitis. If left untreated, the prognosis is poor - a 90% mortality rate within 2 years. Several authors have described the otologic manifestations of WG; these authors, however, have not mentioned the stage of the disease in which these findings present - whether as initial manifestations or subsequent to other findings. Aim: To describe three confirmed cases of WG with mastoiditis as the first manifestation, progressing to peripheral facial paralysis (PFP). Material and Method: A clinical series study. Patients diagnosed with WG that initially presented with otologic findings are described. Results: The three cases presented with unilateral otalgia, otorrhea, and hearing loss associated with ipsilateral PFP. None recovered in spite of the treatment; an investigation of associated diseases was therefore undertaken. Positive ANCA-C titers where detected in all patients, confirming the diagnosis of WG. Clinical improvement was seen after treatment of WG; the PFP regressed and hearing thresholds improved partially. Conclusion: Complications of otitis media (mastoiditis and PFP) that do not respond to the usual treatment require an investigation of associated diseases; WG should be included for an early diagnosis to change the prognosis in these patients.A Granulomatose de Wegener (GW) é caracterizada por granulomas necrotizantes e vasculite. Sem tratamento a doença tem prognóstico pobre com índice de mortalidade de 90% em 2 anos. Diversos autores citam as manifestações otológicas no curso da GW, entretanto não é especificado em que momento da doença elas apareceram, isto é, se como manifestação inicial ou subsequente a outros achados. Objetivo: Descrever três casos confirmados de GW que apresentaram inicialmente mastoidite e evoluíram com paralisia facial periférica (PFP). Material e Método: Estudo de série de casos. Pacientes diagnosticados com GW que apresentaram inicialmente manifestações otológicas são descritos. Resultados: Os três casos descritos abriram o quadro com otalgia, otorreia e hipoacusia unilateral, associada a paralisia facial periférica ipsilateral. Tiveram resposta inadequada aos tratamentos instituídos o que motivou uma investigação de outras doenças associadas. Nessas circunstâncias, detectaram-se títulos positivos de ANCA-C em todos pacientes, confirmando-se o diagnóstico de GW, após período variável de investigação. Institui-se o tratamento para GW observando-se melhora do quadro clínico, regressão da PFP e melhora parcial dos limiares auditivos. Conclusão: Complicações de otites médias agudas (mastoidite e PFP) refratárias as terapêuticas habituais impõem a investigação de doenças associadas e a GW deverá ser pesquisada para que se possa fazer o diagnóstico o mais precocemente possível, alterando desta forma o prognóstico destes pacientes.UNIFESP-EPM Departamento de Otorrinolaringologia e Cirurgia de Cabeça e PescoçoUNIFESP, EPM, Depto. de Otorrinolaringologia e Cirurgia de Cabeça e PescoçoSciEL

Incorporated model of deep features fusion

Abdelaziz, A., & Mahmoud, A. N. (2022). Skin Cancer Detection Using Deep Learning and Artificial Intelligence: Incorporated model of deep features fusion. Fusion: Practice and Applications, 8(2), 8-15. https://doi.org/10.54216/FPA.080201 © 2022, American Scientific Publishing Group (ASPG). All rights reserved.Among the most frequent forms of cancer, skin cancer accounts for hundreds of thousands of fatalities annually throughout the globe. It shows up as excessive cell proliferation on the skin. The likelihood of a successful recovery is greatly enhanced by an early diagnosis. More than that, it might reduce the need for or the frequency of chemical, radiological, or surgical treatments. As a result, savings on healthcare expenses will be possible. Dermoscopy, which examines the size, form, and color features of skin lesions, is the first step in the process of detecting skin cancer and is followed by sample and lab testing to confirm any suspicious lesions. Deep learning AI has allowed for significant progress in image-based diagnostics in recent years. Deep neural networks known as convolutional neural networks (CNNs or ConvNets) are essentially an extended form of multi-layer perceptrons. In visual imaging challenges, CNNs have shown the best accuracy. The purpose of this research is to create a CNN model for the early identification of skin cancer. The backend of the CNN classification model will be built using Keras and Tensorflow in Python. Different network topologies, such as Convolutional layers, Dropout layers, Pooling layers, and Dense layers, are explored and tried out throughout the model's development and validation phases. Transfer Learning methods will also be included in the model to facilitate early convergence. The dataset gathered from the ISIC challenge archives will be used to both tests and train the model.publishersversionpublishe

Using Multi-level Convolutional Neural Network for Classification of Lung Nodules on CT images

© 2018 IEEE. Lung cancer is one of the four major cancers in the world. Accurate diagnosing of lung cancer in the early stage plays an important role to increase the survival rate. Computed Tomography (CT)is an effective method to help the doctor to detect the lung cancer. In this paper, we developed a multi-level convolutional neural network (ML-CNN)to investigate the problem of lung nodule malignancy classification. ML-CNN consists of three CNNs for extracting multi-scale features in lung nodule CT images. Furthermore, we flatten the output of the last pooling layer into a one-dimensional vector for every level and then concatenate them. This strategy can help to improve the performance of our model. The ML-CNN is applied to ternary classification of lung nodules (benign, indeterminate and malignant lung nodules). The experimental results show that our ML-CNN achieves 84.81\% accuracy without any additional hand-craft preprocessing algorithm. It is also indicated that our model achieves the best result in ternary classification

LCDctCNN: Lung Cancer Diagnosis of CT scan Images Using CNN Based Model

The most deadly and life-threatening disease in the world is lung cancer. Though early diagnosis and accurate treatment are necessary for lowering the lung cancer mortality rate. A computerized tomography (CT) scan-based image is one of the most effective imaging techniques for lung cancer detection using deep learning models. In this article, we proposed a deep learning model-based Convolutional Neural Network (CNN) framework for the early detection of lung cancer using CT scan images. We also have analyzed other models for instance Inception V3, Xception, and ResNet-50 models to compare with our proposed model. We compared our models with each other considering the metrics of accuracy, Area Under Curve (AUC), recall, and loss. After evaluating the model's performance, we observed that CNN outperformed other models and has been shown to be promising compared to traditional methods. It achieved an accuracy of 92%, AUC of 98.21%, recall of 91.72%, and loss of 0.328.Comment: 8, accepted by 10th International Conference on Signal Processing and Integrated Networks (SPIN 2023