Early Lung Cancer Detection by Using Artificial Intelligence System

Lung cancer is by far the primary cause of cancer deaths globally. Computer-aided diagnosis (CAD) system is used for the prediction of lung cancer which helps to attain a high detection rate and reduces the time consumed for analyzing the sample. In this paper, CAD system based on sputum color images is proposed which consists of four main processing steps. It starts with the preprocessing step using a heuristic rule-based and a Bayesian classification method using the histogram analysis. In this step, the region of interest (ROI) representing the sputum cell is detected and extracted. In order to segment the nuclei from the cytoplasm, mean shift segmentation is used. The next step is feature analysis. Finally, the diagnosis is done using a rule-based algorithm alongside the artificial neural network (ANN) and support vector machine (SVM) for identifying cancerous and non-cancerous cells. The performance evaluation was done based on the sensitivity, specificity, and accuracy. Our methods are validating by using a set of experiments conducted with a data set of 100 images. The final results showed that the techniques used outperformed conventional methods. The proposed CAD system achieved a reasonable accuracy above 95% with high true positive rates that can basically meet the requirement of clinical diagnosis

Automatic cerebrovascular segmentation methods - a review

Cerebrovascular diseases are one of the serious causes for the increase in mortality rate in the world which affect the blood vessels and blood supply to the brain. In order, diagnose and study the abnormalities in the cerebrovascular system, accurate segmentation methods can be used. The shape, direction and distribution of blood vessels can be studied using automatic segmentation. This will help the doctors to envisage the cerebrovascular system. Due to the complex shape and topology, automatic segmentation is still a challenge to the clinicians. In this paper, some of the latest approaches used for segmentation of magnetic resonance angiography (MRA) images are explained. Some of such methods are deep convolutional neural network (CNN), 3dimentional-CNN (3D-CNN) and 3D U-Net. Finally, these methods are compared for evaluating their performance. 3D U-Net is the better performer among the described methods

Neutrosophic C-Means Clustering with Optimal Machine Learning Enabled Skin Lesion Segmentation and Classification

Early detection and classification of skin lesions using dermoscopic images have attracted significant attention in the healthcare sector. Automated skin lesion segmentation becomes tedious owing to the presence of artifacts like hair, skin line, etc. Earlier works have developed skin lesion det ection models using clustering approaches. The advances in neutrosophic set (NS) models can be applied to derive effective clustering models for skin lesion segmentation. At the same time, artificial intelligence (AI) tools can be developed for the identification and categorization of skin cancer using dermoscopic images. This article introduces a Neutrosophic C-Means Clustering with Optimal Machine Learning Enabled Skin Lesion Segmentation and Classification (NCCOML-SKSC) model. The proposed NCCOML-SKSC model derives a NCC-based segmentation approach to segment the dermoscopic images. Besides, the AlexNet model is exploited to generate a feature vector. In the final stage, the optimal multilayer perceptron (MLP) model is utilized for the classification process in which the MLP parameters are chosen by the use of a whale optimization algorithm (WOA). A detailed experimental analysis of the NCCOML-SKSC model using a benchmark dataset is performed and the results highlighted the supremacy of the NCCOML-SKSC model over the recent approaches

Cerebrovascular segmentation from MRA images

There is provided a method of processing a cerebrovascular medical image, the method comprising receiving magnetic resonance angiography (MRA) image associated with a cerebrovascular tissue comprising blood vessels and brain tissues other than blood vessels; segmenting MRA image using a prior appearance model for generating first prior appearance features representing a first-order prior appearance model and second appearance features representing a second-order prior appearance model of the cerebrovascular tissue, wherein current appearance model comprises a 3D Markov-Gibbs Random Field (MGRF) having a 2D rotational and translational symmetry such that MGRF model is 2D rotation and translation invariant; segmenting MRA image using current appearance model for generating current appearance features distinguishing blood vessels from other brain tissues; adjusting MRA image using first and second prior appearance features and current appearance futures; and generating an enhanced MRA image based on said adjustment. There is also provided a system for doing the same. Application US16/159,790 events 2018-10-15 Application filed by Zayed University 2018-10-15 Priority to US16/159,790 2018-10-15 Assigned to Zayed University 2020-04-16 Publication of US20200116808A1 2020-09-08 Application granted 2020-09-08 Publication of US10768259B2 Status Active 2039-03-02 Adjusted expiratio

Early detection of lung cancer - A challenge

Lung cancer or lung carcinoma, is a common and serious type of cancer caused by rapid cell growth in tissues of the lung. Lung cancer detection at its earlier stage is very difficult because of the structure of the cell alignment which makes it very challenging. Computed tomography (CT) scan is used to detect the presence of cancer and its spread. Visual analysis of CT scan can lead to late treatment of cancer; therefore, different steps of image processing can be used to solve this issue. A comprehensive framework is used for the classification of pulmonary nodules by combining appearance and shape feature descriptors, which helps in the early diagnosis of lung cancer. 3D Histogram of Oriented Gradient (HOG), Resolved Ambiguity Local Binary Pattern (RALBP) and Higher Order Markov Gibbs Random Field (MGRF) are the feature descriptors used to explain the nodule’s appearance and compared their performance. Lung cancer screening methods, image processing techniques and nodule classification using radiomic-based framework are discussed in this paper which proves to be very effective in lung cancer prediction. Good performance is shown by using RALBP descriptor

Medical images protection and authentication using hybrid DWT-DCT and SHA256-MD5 hash functions

© 2017 IEEE. This paper deals with a blind digital watermarking technique for the ownership protection and content authentication of X-ray and MRI medical images. Extreme care is required before embedding watermarking information in medical images, to protect the image quality to avoid the wrong diagnosis. The proposed watermarking technique contains a robust watermark for the ownership protection and fragile watermarks for the content authentication. In the watermarking technique, the medical image is divided into regions and the watermark information is embedded in both the transform domain and the spatial domain. The proposed watermarking technique was successfully tested on a variety of X-ray and MRI medical images and offered high peak signal to noise ratios, similarity structure index measure values and wavelet domain signal to noise rations

A Review on the Cerebrovascular Segmentation Methods

© 2018 IEEE. This paper explores various methods that have been proposed for the segmentation of the cerebrovascular structure. All of the methods listed are a combination old, new, automatic and semiautomatic models that produce promising results. Each method will be explained along with its advantages and disadvantages. Each of the methods explained are further explored in this paper with variety algorithms produced by using certain models to target certain areas in the cerebrovascular structure. These algorithms were developed to segment cerebrovascular structures from scans obtained from various image modalities e.g., time of flight magnetic-resonance angiography (TOF-MRA), and computed tomography angiography (CTA)

Early Diagnosis and Staging of Prostate Cancer Using Magnetic Resonance Imaging: State of the Art and Perspectives

Prostate cancer is the second most common cancer among men in the United States after skin cancer. Although it can be a serious disease, early diagnosis of prostate cancer can significantly prevent the growth of cancerous cells. The feature extraction is the process of defining and deriving from the prostate region computational entities that form a sort of prostate cancer signature. Full computer-aided diagnosis (CAD) systems presented in several studies have reported the use of engineered features obtained from multimodal magnetic resonance imaging (MRI) to detect prostate cancer. Similar to other medical imaging CAD systems, the computer-aided diagnosis of prostate cancer using MRI framework encompasses four stages, namely: pre-processing, prostate region extraction, features extraction, and classification. Identifying the region of interest in the MR images is essential to reduce the complexity of the next stages and enhance the performance of the overall CAD system

Mapping Agricultural Soil in Greenhouse Using an Autonomous Low-Cost Robot and Precise Monitoring

Our work is focused on developing an autonomous robot to monitor greenhouses and large fields. This system is designed to operate autonomously to extract useful information from the plants based on precise GPS localization. The proposed robot is based on an RGB camera for plant detection and a multispectral camera for extracting the different special bands for processing, and an embedded architecture integrating a Nvidia Jetson Nano, which allows us to perform the required processing. Our system uses a multi-sensor fusion to manage two parts of the algorithm. Therefore, the proposed algorithm was partitioned on the CPU-GPU embedded architecture. This allows us to process each image in 1.94 s in a sequential implementation on the embedded architecture. The approach followed in our implementation is based on a Hardware/Software Co-Design study to propose an optimal implementation. The experiments were conducted on a tomato farm, and the system showed that we can process different images in real time. The parallel implementation allows to process each image in 36 ms allowing us to satisfy the real-time constraints based on 5 images/s. On a laptop, we have a total processing time of 604 ms for the sequential implementation and 9 ms for the parallel processing. In this context, we obtained an acceleration factor of 66 for the laptop and 54 for the embedded architecture. The energy consumption evaluation showed that the prototyped system consumes a power between 4 W and 8 W. For this raison, in our case, we opted a low-cost embedded architecture based on Nvidia Jetson Nano