Search Results - lesion classification technique

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  1. 1
    Stroke Lesion Segmentation And Classification For Diffusion-Weighted Imaging

    Stroke Lesion Segmentation And Classification For Diffusion-Weighted Imaging by Mohd Noor, Nor Shahirah

    Published 2020
    “…In conclusion, the proposed stroke classification technique has the potential to diagnose and classify brain stroke lesions.…”
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  2. 2
    Brain Lesion Segmentation And Classification Using Diffusion-Weighted Imaging (DWI)

    Brain Lesion Segmentation And Classification Using Diffusion-Weighted Imaging (DWI) by Muda, Ayuni Fateeha

    Published 2016
    “…Out of various MRI techniques, Diffusion-Weighted Imaging (DWI) remains the most accurate technique for early detection and discrimination of several brain lesions such as stroke. …”
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  3. 3
    Multiclass classification for chest x-ray images based on lesion location in lung zones

    Multiclass classification for chest x-ray images based on lesion location in lung zones by Saad, Mohd Nizam, Muda, Zurina, Sahari, Noraidah, Abd Hamid, Hamzaini

    Published 2015
    “…Innovation in radiology technology has generated numerous kinds of medical images like the chest X-ray (CXR).This image is used to find common problem in lung like the lesion through scanning process in lung area which is divided into six zones.By classifying the CXR images with common feature like the lesion location, we can ensure efficient image retrieval.Recently, Support Vector Machine (SVM) has turn out to be a well-known method for image classification.While many previous studies have reported the achievement of SVM in classifying images, yet there is still problem with this technique for multiclass classification.Since SVM is a binary classification technique, its ability is limited to classifying features between two classes at one time. …”
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  4. 4
    Automated Segmentation And Classification Technique For Brain Stroke

    Automated Segmentation And Classification Technique For Brain Stroke by Mohd Saad, Norhashimah, Abdullah, Abdul Rahim, Mohd Noor, Niza Suzaini, Mohd Ali, Nursabillilah

    Published 2019
    “…This study proposes a segmentation and classification technique to detect brain stroke lesions based on diffusion-weighted imaging (DWI). …”
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  5. 5
    Automatic Brain Lesion Detection And Classification Based On Diffusion-Weighted Imaging Using Adaptive Thresholding And A Rule-Based Classifier

    Automatic Brain Lesion Detection And Classification Based On Diffusion-Weighted Imaging Using Adaptive Thresholding And A Rule-Based Classifier by Mohd Saad, Norhashimah, Abu-Bakar, Syed A.R., Muda, A.F., Muda, Sobri, Ahmad Radzi, Syafeeza

    Published 2014
    “…Overall,adaptive thresholding provides high segmentation performance for hyper-intensity lesions.The best segmentation and classification performance is achieved for acute stroke.The establishment of the technique could be used to automate the diagnosis and to clearly understand major brain lesions. …”
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  6. 6
    Review Of Brain Lesion Detection And Classification Using Neuroimaging Analysis Techniques

    Review Of Brain Lesion Detection And Classification Using Neuroimaging Analysis Techniques by Mohd Saad, Norhashimah, Abu Bakar, Syed Abdul Rahman Syed, Muda, Ahmad Sobri, Mokjib, Musa Mohd

    Published 2015
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  7. 7
    Automated diagnosis of focal liver lesions using bidirectional empirical mode decomposition features

    Automated diagnosis of focal liver lesions using bidirectional empirical mode decomposition features by Acharya, U. Rajendra, Koh, Joel En Wei, Hagiwara, Yuki, Tan, Jen Hong, Gertych, Arkadiusz, Vijayananthan, Anushya, Yaakup, Nur Adura, Abdullah, Basri Johan Jeet, Mohd Fabell, Mohd Kamil, Yeong, Chai Hong

    Published 2018
    “…The accuracy, sensitivity, and specificity of lesion classification were 92.95%, 90.80%, and 97.44%, respectively. …”
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  8. 8
    Automated stroke lesion detection and diagnosis system

    Automated stroke lesion detection and diagnosis system by Mohd Saad, N., M. Noor, N. S., Abdullah, A. R., Muda, Ahmad Sobri, Muda, A. F., Abdul Rahman, N. N. S.

    Published 2017
    “…This study proposes a technique for automated detection and diagnosis of stroke lesions based on diffusion-weighted imaging (DWI). …”
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  9. 9
    Automated detection of breast cancer in mammography images using Gabor filter and Bayesian classifier / Bardia Yousefi

    Automated detection of breast cancer in mammography images using Gabor filter and Bayesian classifier / Bardia Yousefi by Bardia, Yousefi

    Published 2012
    “…The objective of this research is developing a technique for feature extraction in breast lesions by applying Gabor filter and using Bayesian classifier for classification of the breast lesions. …”
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  10. 10
    Novel techniques for enhancement and segmentation of acne vulgaris lesions

    Novel techniques for enhancement and segmentation of acne vulgaris lesions by Malik, A. S., Humayun, J., Kamel, N., Yap, F. B.-B.

    Published 2013
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  11. 11
    Development of Hybrid Convolutional Neural Network and Auto-Regressive Integrated Moving Average for Skin Cancer Classification

    Development of Hybrid Convolutional Neural Network and Auto-Regressive Integrated Moving Average for Skin Cancer Classification by KA CHIN, CHEE

    Published 2022
    “…The suitable image pre-processing technique and image augmentation method are identified to avoid the degradation of classification accuracy in this research. …”
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  12. 12
    A Novel Algorithm for Breast Lesion Detection Using Textons and Local Configuration Pattern Features With Ultrasound Imagery

    A Novel Algorithm for Breast Lesion Detection Using Textons and Local Configuration Pattern Features With Ultrasound Imagery by Acharya, U. Rajendra, Meiburger, Kristen M., Wei Koh, Joel En, Ciaccio, Edward J., Arunkumar, N., See, Mee Hoong, Mohd Taib, Nur Aishah, Vijayananthan, Anushya, Rahmat, Kartini, Fadzli, Farhana, Leong, Sook Sam, Westerhout, Caroline Judy, Chantre-Astaiza, Angela, Ramirez-Gonzalez, Gustavo

    Published 2019
    “…This paper presents a novel method for the detection of breast lesions in ultrasound images using texton filter banks, local configuration pattern features, and classification, without employing any segmentation technique. …”
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  13. 13
    Thyroid lesion classification in 242 patient population using Gabor transform features from high resolution ultrasound images

    Thyroid lesion classification in 242 patient population using Gabor transform features from high resolution ultrasound images by Acharya, U.R., Chowriappa, P., Fujita, H., Bhat, S., Dua, S., Koh, J.E.W., Eugene, L.W.J., Kongmebhol, P., Ng, K.H.

    Published 2016
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  14. 14
    Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images

    Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images by Adil Humayun, Khan

    Published 2024
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  15. 15
    Application Of Artificial Neural Networks in the Classification of Cervical Cells Based on the Bethesda System

    Application Of Artificial Neural Networks in the Classification of Cervical Cells Based on the Bethesda System by Mat Isa, Nor Ashidi, Mashor, Mohd Yusoff, Othman, Nor Hayati, Zamli, Kamal Zuhairi

    Published 2006
    “…In the second stage, we classify cervical cells abnormality into three classes based on The Bethesda Classification System; normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). …”
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  16. 16
    Computer-aided screening system for cervical precancerous cells based on field emission scanning electron microscopy and energy dispersive x-ray / Yessi Jusman

    Computer-aided screening system for cervical precancerous cells based on field emission scanning electron microscopy and energy dispersive x-ray / Yessi Jusman by Yessi, Jusman

    Published 2016
    “…Finally, for the classification stage, the Discriminant Analysis (DA) concept was used to classify the cervical precancerous cells based on the Bethesda system (i.e. normal, Low-grade Squamous Intraepithelial Lesion (LSIL) and High-grade Squamous Intraepithelial Lesion (HSIL)). …”
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  17. 17
    Development of a CAD system for stroke diagnosis using machine learning on DWI-MRI images

    Development of a CAD system for stroke diagnosis using machine learning on DWI-MRI images by Mohd Saad, Norhashimah, Azman, Izzatul Husna, Abdullah, Abdul Rahim, Hamzah, Rostam Affendi, Muda, Ahmad Sobri, Yamba, Farzanah Atikah

    Published 2025
    “…A hybrid segmentation technique, fuzzy c-means with active contour (FCMAC), is proposed to enhance lesion localization accuracy. …”
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  18. 18
    Detection of lesion of lung cancer in CT scan images using watershed segmentation / Nur Najihah Sofia Mohd Marzuki …[et al.]

    Detection of lesion of lung cancer in CT scan images using watershed segmentation / Nur Najihah Sofia Mohd Marzuki …[et al.] by Mohd Marzuki, Nur Najihah Sofia, Sulaiman, Siti Noraini, Isa, Iza Sazanita, A.Karim, Noor Khairiah, Md Tahir, Nooritawati

    Published 2021
    “…The lung lobes and nodules or lesion in CT image are segmented using two techniques which are convolution watershed and modified watershed within two stage approaches. …”
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  19. 19
    Effect of color feature normalization on segmentation of color images

    Effect of color feature normalization on segmentation of color images by Khan, J., Malik, A.S., Kamel, N., Das, S.C., Affandi, A.M.

    Published 2017
    “…In this paper, an unsupervised technique is proposed for the segmentation of acne vulgaris lesions. …”
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  20. 20
    Segmentation and classification of skin cancer diseases based on deep learning: challenges and future directions

    Segmentation and classification of skin cancer diseases based on deep learning: challenges and future directions by Dira Khalaf, Abdulrahman, Hamdan, Hazlina, Abdul Halin, Alfian, Manshor, Noridayu

    Published 2025
    “…This study explores the application of DL in skin cancer diagnosis, focusing on advances in image segmentation and classification. DL-based models are reviewed specifically by convolutional neural networks (CNNs), and evaluations on their effectiveness for skin lesion detection are provided. …”
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