Search Results - (( lesion classification using algorithm ) OR ( using function learning algorithm ))

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

“…After which, the extracted features were subjected to particle swarm optimization (PSO) technique for the selection of a set of optimized features for classification. Our automated CAD system can differentiate normal, malignant, and benign liver lesions using machine learning algorithms. …”
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Leaf lesion classification (LLC) algorithm based on artificial bee colony (ABC) by Ahmad, Faudziah, Ku-Mahamud, Ku Ruhana, Sainin, Mohd Shamrie, Airuddin, Ahmad

“…Results showed that the Leaf Lesion Classification (LLC) algorithm based on Artificial Bee colony (ABC) produced an average 96.83% of accuracy and average 1.66 milliseconds of processing time, indicating that LLC algorithm is better than algorithm such as Otsu, Canny, Roberts and Sobel. …”
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Automated feature extraction on brain MRI images for predicting multiple sclerosis patient disability by M. Muslim, Ali

“…From the large-scale features, a correlation analysis is performed to select the highly correlated features used for predicting patients’ disability. This was based on machine learning and regression algorithms at the first phase. …”
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Novel techniques for enhancement and segmentation of acne vulgaris lesions by Malik, A. S., Humayun, J., Kamel, N., Yap, F. B.-B.

“…Conclusion: This article specifically discusses the contrast enhancement and segmentation for automated diagnosis system of acne vulgaris lesions. The results are promising that can be used for further classification of acne vulgaris lesions for final grading of the lesions.…”
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STATISTICAL FEATURE LEARNING THROUGH ENHANCED DELAUNAY CLUSTERING AND ENSEMBLE CLASSIFIERS FOR SKIN LESION SEGMENTATION AND CLASSIFICATION by Adil H., Khan, Dayang Nurfatimah, Awang Iskandar, Jawad F., Al-Asad, SAMIR, EL-NAKLA, SADIQ A., ALHUWAIDI

“…A boost ensemble learning algorithm using Support Vector Machines (SVM) as initial classifiers and Artificial Neural Networks (ANN) as a final classifier is employed to learn the patterns of different skin lesion class features. …”
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Automated Segmentation And Classification Technique For Brain Stroke by Mohd Saad, Norhashimah, Abdullah, Abdul Rahim, Mohd Noor, Niza Suzaini, Mohd Ali, Nursabillilah

“…This study proposes a segmentation and classification technique to detect brain stroke lesions based on diffusion-weighted imaging (DWI). …”
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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

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

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Ensemble learning of deep learning and traditional machine learning approaches for skin lesion segmentation and classification by Adil H., Khan, Dayang Nurfatimah, Awang Iskandar, Jawad F., Al-Asad, Hiren, Mewada, Muhammad Abid, Sherazi

“…After that segmented region is classified into three types of skin lesion using hybrid features of Alex-Net and VGG-16 through the transfer learning approach. …”
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Identifying melanoma characteristics using directional imaging algorithm and convolutional neural network on dermoscopic images / Mohammad Asaduzzaman Rasel by Mohammad Asaduzzaman , Rasel

“…This research is divided into two phases – 1) Feature Engineering phase explains skin conditions based on lesion segmentation and different dermoscopic feature extraction, while 2) Classification phase detects Melanoma. …”
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Feature selection and classification of ulcerated lesions using statistical analysis for WCE images by Suman, S., Hussin, F.A., Malik, A.S., Ho, S.H., Hilmi, I., Leow, A.H.-R., Goh, K.-L.

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Digital segmentation of skin diseases / Hadzli Hashim and Razali Abdul Hadi by Hashim, Hadzli, Abdul Hadi, Razali

“…RGB colour variegations are useful features used by the domain's experts in their morphological learning method for skin disease classification. …”
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An efficient AdaBoost algorithm for enhancing skin cancer detection and classification by Gamil, Seham, Zeng, Feng, Alrifaey, Moath, Asim, Muhammad, Ahmad, Naveed

“…To improve accuracy, the AdaBoost algorithm is utilized, which amalgamates weak classification models into a robust classifier with high accuracy. …”
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Automated Detection and Classification of Retinal Vein Occlusion Using Ultra-widefield Retinal Fundus Images and Transfer Learning by Ivy Ong Siaw Yin, Ong

“…The study also evaluates the classification model trained with lesion masks to classify images accurately into the respective categories. …”
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A Reference Based Surface Defect Segmentation Algorithm For Automatic Optical Inspection System by Wong, Ze-Hao

“…However, present complex algorithms which are accurate require high processing power using a large size of learning dataset without labelling error. …”
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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

“…A hybrid segmentation technique, fuzzy c-means with active contour (FCMAC), is proposed to enhance lesion localization accuracy. For classification, the system evaluates traditional machine learning algorithms like support vector machine (SVM) and k-nearest neighbor (KNN), alongside deep learning models such as convolutional neural network (CNN) and bilayered neural network (BNN). …”
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Training functional link neural network with ant lion optimizer by Mohmad Hassim, Yana Mazwin, Ghazali, Rozaida

“…Functional Link Neural Network (FLNN) has becoming as an important tool used in machine learning due to its modest architecture. …”
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Functional link neural network with modified bee-firefly learning algorithm for classification task by Mohmad Hassim, Yana Mazwin

“…The single layer property of FLNN also make the learning algorithm used less complicated compared to MLP network. …”
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A modified generalized RBF model with EM-based learning algorithm for medical applications by Ma, Li Ya, Abdul Rahman, Abdul Wahab, Quek, Chai

“…Radial Basis Function (RBF) has been widely used in different fields, due to its fast learning and interpretability of its solution. …”
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An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction by Shah, Habib

“…Furthermore, here these algorithms used to train the MLP on two tasks; the seismic event's prediction and Boolean function classification. …”
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