Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The early detection of KT has significant benefits in reducing death rates, producing preventive measures that reduce effects, and overcoming the tumor. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of deep learning (DL) can save diagnosis time, improve test accuracy, reduce costs, and reduce the radiologist's workload. In this paper, we present detection models for diagnosing the presence of KTs in computed tomography (CT) scans. Toward detecting and classifying KT, we proposed 2D-CNN models; three models are concerning KT detection such as a 2D convolutional neural network with six layers (CNN-6), a ResNet50 with 50 layers, and a VGG16 with 16 layers. The last model is for KT classification as a 2D convolutional neural network with four layers (CNN-4). In addition, a novel dataset from the King Abdullah University Hospital (KAUH) has been collected that consists of 8,400 images of 120 adult patients who have performed CT scans for suspected kidney masses. The dataset was divided into 80% for the training set and 20% for the testing set. The accuracy results for the detection models of 2D CNN-6 and ResNet50 reached 97%, 96%, and 60%, respectively. At the same time, the accuracy results for the classification model of the 2D CNN-4 reached 92%. Our novel models achieved promising results; they enhance the diagnosis of patient conditions with high accuracy, reducing radiologist's workload and providing them with a tool that can automatically assess the condition of the kidneys, reducing the risk of misdiagnosis. Furthermore, increasing the quality of healthcare service and early detection can change the disease's track and preserve the patient's life.

/pdf/kidney-tumor-detection-and-classification-based-on-deep-2uni4lii.pdf

Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans

Deep learning models for tuberculosis detection and infected region visualization in chest X-ray images

Tuberculosis (TB) was the leading infectious cause of mortality globally prior to COVID-19 and chest radiography has an important role in the detection, and subsequent diagnosis, of patients with this disease. The conventional experts reading has substantial within- and between-observer variability, indicating poor reliability of human readers. Substantial efforts have been made in utilizing various artificial intelligence-based algorithms to address the limitations of human reading of chest radiographs for diagnosing TB.This systematic literature review (SLR) aims to assess the performance of machine learning (ML) and deep learning (DL) in the detection of TB using chest radiography (chest x-ray [CXR]).In conducting and reporting the SLR, we followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 309 records were identified from Scopus, PubMed, and IEEE (Institute of Electrical and Electronics Engineers) databases. We independently screened, reviewed, and assessed all available records and included 47 studies that met the inclusion criteria in this SLR. We also performed the risk of bias assessment using Quality Assessment of Diagnostic Accuracy Studies version 2 (QUADAS-2) and meta-analysis of 10 included studies that provided confusion matrix results.Various CXR data sets have been used in the included studies, with 2 of the most popular ones being Montgomery County (n=29) and Shenzhen (n=36) data sets. DL (n=34) was more commonly used than ML (n=7) in the included studies. Most studies used human radiologist's report as the reference standard. Support vector machine (n=5), k-nearest neighbors (n=3), and random forest (n=2) were the most popular ML approaches. Meanwhile, convolutional neural networks were the most commonly used DL techniques, with the 4 most popular applications being ResNet-50 (n=11), VGG-16 (n=8), VGG-19 (n=7), and AlexNet (n=6). Four performance metrics were popularly used, namely, accuracy (n=35), area under the curve (AUC; n=34), sensitivity (n=27), and specificity (n=23). In terms of the performance results, ML showed higher accuracy (mean ~93.71%) and sensitivity (mean ~92.55%), while on average DL models achieved better AUC (mean ~92.12%) and specificity (mean ~91.54%). Based on data from 10 studies that provided confusion matrix results, we estimated the pooled sensitivity and specificity of ML and DL methods to be 0.9857 (95% CI 0.9477-1.00) and 0.9805 (95% CI 0.9255-1.00), respectively. From the risk of bias assessment, 17 studies were regarded as having unclear risks for the reference standard aspect and 6 studies were regarded as having unclear risks for the flow and timing aspect. Only 2 included studies had built applications based on the proposed solutions.Findings from this SLR confirm the high potential of both ML and DL for TB detection using CXR. Future studies need to pay a close attention on 2 aspects of risk of bias, namely, the reference standard and the flow and timing aspects.PROSPERO CRD42021277155; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=277155. 

/pdf/machine-and-deep-learning-for-tuberculosis-detection-on-qktndpqt.pdf

Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review

Data analytics using artificial intelligence is the process of leveraging advanced AI techniques to extract insights and knowledge from large and complex datasets [...]

/pdf/big-data-analytics-using-artificial-intelligence-20gy6bo7.pdf

Big Data Analytics Using Artificial Intelligence

From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning

Tuberculosis (TB) is an infectious disease that has been a major menace to human health globally, causing millions of deaths yearly. Well-timed diagnosis and treatment are an arch to full recovery of the patient. Computer-aided diagnosis (CAD) has been a hopeful choice for TB diagnosis. Many CAD approaches using machine learning have been applied for TB diagnosis, specific to the artificial intelligence (AI) domain, which has led to the resurgence of AI in the medical field. Deep learning (DL), a major branch of AI, provides bigger room for diagnosing deadly TB disease. This review is focused on the limitations of conventional TB diagnostics and a broad description of various machine learning algorithms and their applications in TB diagnosis. Furthermore, various deep learning methods integrated with other systems such as neuro-fuzzy logic, genetic algorithm, and artificial immune systems are discussed. Finally, multiple state-of-the-art tools such as CAD4TB, Lunit INSIGHT, qXR, and InferRead DR Chest are summarized to view AI-assisted future aspects in TB diagnosis.

/pdf/evolution-of-machine-learning-in-tuberculosis-diagnosis-a-39osbnbp.pdf

Evolution of Machine Learning in Tuberculosis Diagnosis: A Review of Deep Learning-Based Medical Applications

Lysophosphatidic acid (LPA) is a bioactive signaling molecule that activates LPA1 receptors, which are over expressed in breast cancer. This signaling cascade promotes cancer cell proliferation, survival, metastasis, and angiogenesis. Small molecules that block LPA1 signaling could benefit breast cancer therapy. Novel 1,2,4-triazoles and 1,3,4-oxadiazoles conjugates of benzimidazole/oxazole/thiazole were designed as LPA1 receptor inhibitors, synthesized, and the in-vitro anticancer activity was examined through breast carcinoma cells (MCF-7) by MTT assay. The compounds that reduced the proliferation of MCF-7 cells were further evaluated for their effects on apoptosis, migration, and angiogenesis at IC50 concentration. Suppression of LPA1 protein and gene expression was analyzed by western blot and qRT-PCR techniques. In addition, ligand interactions towards protein and ligand-receptor stability were predicted by molecular interaction studies and molecular trajectory analysis, respectively. The compounds BI-4 and OX-1 exhibited varying levels of cytotoxicity, with IC50 values of 138 μM and 83.25 μM, respectively. They were able to effectively reduce migration and apoptosis as confirmed by AO/EB staining assay. In addition, these compounds were found to significantly reduce the protein and gene expression of LPA1 and angiogenesis genes (Angiogenin-II and Vegf) in MCF-7 cells. Synthesized ligands also showed interactions with the LPA1 receptor and demonstrated good stability in MD simulation. Overall, BI-4 and OX-1 hold promise as potential lead compounds for further exploration of the role of LPA1 receptors in the management of breast carcinoma. 

New LPA1 receptor modulators: Design, synthesis, in-silico, and anticancer studies of triazole and oxadiazole analogs

Aim: Breast cancer has been a leading cause of mortality among women worldwide in recent years. Targeting the lysophosphatidic acid (LPA)-LPA1 pathway using small molecules could improve breast cancer therapy. Materials & methods: Thiazolidin-4-ones were developed and tested on MCF-7 cancer cells, and active compounds were analyzed for their effects on apoptosis, migration angiogenesis and LPA1 protein and gene expression. Results & conclusion: Compounds TZ-4 and TZ-6 effectively reduced the migration of MCF-7 cells, and induced apoptosis. TZ-4, TZ-6, TZ-8 and TZ-14 significantly reduced the LPA1 protein, LPA1 and angiogenesis gene expression in treated MCF-7 cells. Molecular docking and molecular dynamic simulation studies reveal the ligand interactions and stability of the LPA1-ligand complex. Developed thiazolidin-4-ones showed great potential as an LPA1-targeted approach to combating breast cancer.

Design, synthesis and evaluation of new thiazolidin-4-ones as LPA1 receptor antagonists for breast cancer therapy.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Sethu Arun Kumar

Author Tools

Chat about Author

Papers

Evolution of Machine Learning in Tuberculosis Diagnosis: A Review of Deep Learning-Based Medical Applications

New LPA1 receptor modulators: Design, synthesis, in-silico, and anticancer studies of triazole and oxadiazole analogs

Design, synthesis and evaluation of new thiazolidin-4-ones as LPA1 receptor antagonists for breast cancer therapy.