A5016060450- AI in cancer detection
- Medical Image Segmentation Techniques
- Radiomics and Machine Learning in Medical Imaging
- Medical Imaging Techniques and Applications
- Image Retrieval and Classification Techniques
- Advanced X-ray and CT Imaging
- Retinal Imaging and Analysis
- Generative Adversarial Networks and Image Synthesis
- Advanced Image Fusion Techniques
- Brain Tumor Detection and Classification
- Geological Modeling and Analysis
- Cell Image Analysis Techniques
- Cerebrovascular and Carotid Artery Diseases
- Occupational Health and Safety Research
- Infrared Target Detection Methodologies
- Advanced Image and Video Retrieval Techniques
- Optical Wireless Communication Technologies
- Music and Audio Processing
- Neural Networks and Applications
- Radio Wave Propagation Studies
- BIM and Construction Integration
- Cutaneous Melanoma Detection and Management
- Handwritten Text Recognition Techniques
- Time Series Analysis and Forecasting
- Digital Radiography and Breast Imaging
University of Wisconsin–Madison
2024
Shenzhen University Health Science Center
2019-2020
Universiti Teknologi Petronas
2014-2017
Petronas (Malaysia)
2014-2016
Over 85 million computed tomography (CT) scans are performed annually in the US, of which approximately one quarter focus on abdomen. Given current shortage both general and specialized radiologists, there is a large impetus to use artificial intelligence alleviate burden interpreting these complex imaging studies while simultaneously using images extract novel physiological insights. Prior state-of-the-art approaches for automated medical image interpretation leverage vision language models...
The delays in the construction industry are a global phenomenon and considered as one of most persistent problems throughout world. Pakistan is also no exception to it. key controlling features time, cost, quality safety for project adversely affected by impacts such delays. have many after effects among which main time extension, cost overrun, disputes, arbitrations litigations. purpose this study identify that result extension factors completion. Earlier studies mostly emphasized on major...
Melanoma is one the most increasing cancers since past decades. For accurate detection and classification, discriminative features are required to distinguish between benign malignant cases. In this study, authors introduce a fusion of structural textural from two descriptors. The extracted wavelet curvelet transforms, whereas different variants local binary pattern operator. proposed method implemented on 200 images dermoscopy database including 160 non‐melanoma 40 melanoma images, where...
This paper presents a method for classification of normal and abnormal tissues in mammograms using deep learning approach. VGG-16 CNN architecture with convolutional filter (3×3) is implemented on ROIs from the IRMA dataset. The feature matrix computed first fully connected layer. results are evaluated 10 fold cross validation SVM, binary trees, simple logistics KNN (with k=1, 3, 5) classifiers. produced 100% accuracies AUC 1.0.
Image texture analysis plays an important role in object detection and recognition image processing. The can be used for early of breast cancer by classifying the mammogram images into normal abnormal classes. This study investigates using features obtained from grey level cooccurrence matrices (GLCM) curvelet sub-band levels combined with feature itself. GLCM were constructed each three decomposition levels. vector presented to classifier differentiate between tissues. proposed method is...
Views Icon Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Twitter Facebook Reddit LinkedIn Tools Reprints and Permissions Cite Search Site Citation Faouzi Adjed, Ibrahima Faye, Fakhreddine Ababsa, Syed Jamal Gardezi, Sarat Chandra Dass; Classification of skin cancer images using local binary pattern SVM classifier. AIP Conf. Proc. 28 November 2016; 1787 (1): 080006. https://doi.org/10.1063/1.4968145 Download citation file: Ris (Zotero) Reference Manager...
This paper presents a method for classification of normal and abnormal tissues in mammograms using curvelet transform. The coefficients are represented into certain groups coefficients, independently. Some statistical features calculated each group coefficients. These combined with extracted from the mammogram image itself. To improve rate, feature ranking is applied to select most significant features. results support vector machine (SVM) 10-fold cross validation presented. show that ranked...
Detection of breast cancer at early stages helps reducing the mortality rates in women. Mammography has proven to be very useful tool diagnosis cancer, yet there are complications separate diverse morphological features mammographic images. This study investigates potential use component analysis (MCA) for classifying normal and abnormal tissues mammograms. Two different dictionaries i.e. Local discrete cosine transform (LDCT) Curvelet via wrapping (CURVwrap) were used with varying...
Gliomas are the most dominant and lethal type of brain tumors. Growth prediction is significant to quantify tumor aggressiveness, improve therapy planning, estimate patients' survival time. This commonly addressed in literature using mathematical models guided by multi-time point scans multi/single-modal data for same subject. However, these mechanism-based heavily rely on complicated formulations partial differential equations with few parameters that insufficient capture different patterns...
Purpose: To develop and evaluate a transformer-based deep learning model for the synthesis of nephrographic phase images in CT urography (CTU) examinations from unenhanced urographic phases. Materials Methods: This retrospective study was approved by local Institutional Review Board. A dataset 119 patients (mean $\pm$ SD age, 65 12 years; 75/44 males/females) with three-phase studies curated development. The three phases each patient were aligned an affine registration algorithm. custom...
Objective: Automated segmentation tools are useful for calculating kidney volumes rapidly and accurately. Furthermore, these have the power to facilitate large-scale image-based artificial intelligence projects by generating input labels, such as image registration algorithms. Prior automated models largely ignored non-contrast computed tomography (CT) imaging. This work aims implement train a deep learning (DL) model segment kidneys cystic renal lesions (CRLs) from CT scans. Methods: Manual...
Over 85 million computed tomography (CT) scans are performed annually in the US, of which approximately one quarter focus on abdomen. Given current radiologist shortage, there is a large impetus to use artificial intelligence alleviate burden interpreting these complex imaging studies. Prior state-of-the-art approaches for automated medical image interpretation leverage vision language models (VLMs). However, VLMs generally limited 2D images and short reports, do not electronic health record...
A multitude of individuals across the globe grapple with motor disabilities. Neural prosthetics utilizing Brain-Computer Interface (BCI) technology exhibit promise for improving rehabilitation outcomes. The intricate nature EEG data poses a significant hurdle current BCI systems. Recently, qualitative repository signals tied to both upper and lower limb execution imagery tasks has been unveiled. Despite this, productivity Machine Learning (ML) Models that were trained on this dataset was...
Early detection of breast cancer helps reducing the mortality rates. Mammography is very useful tool in detection. But it difficult to separate different morphological features mammographic images. In this study, Morphological Component Analysis (MCA) method used extract aspects images by effectively preserving characteristics regions. MCA decomposes mammogram into piecewise smooth part and texture using Local Discrete Cosine Transform (LDCT) Curvelet via wrapping (CURVwrap). simple...
Wireless infrared communications (WIR) is explained as the light waves propagation in free space by means of radiation which exists 400–700nm.This range corresponds to high frequency useful for higher data rate applications. Therefore, wireless applied rates applications such computing, video and multimedia communication Introduced Gfeller, this field has grown with various link configurations, improved transmitter efficiency, receiver responsivity multiple access techniques quality. Then...