Deep and Hybrid Learning Technique for Early Detection of ...

Deep and Hybrid Learning Technique for Early Detection of ... - MDPI

This study proposes two different approaches with two systems each to diagnose tuberculosis from two datasets.

(PDF) Deep and Hybrid Learning Technique for Early Detection of ...

(a) ResNet-50, GLCM, DWT and LBP. (b) GoogLeNet, GLCM, DWT and LBP. ... obtained an overall accuracy of 99.3%. ... hybrid features, which achieved ...

Deep and hybrid learning of MRI diagnosis for early detection of the ...

The third proposed system is to diagnose the data set using a hybrid technology between ResNet-18 and AlexNet models to extract feature maps and machine ...

Deep and hybrid learning of MRI diagnosis for early detection of the ...

Alzheimer's, or so-called dementia, is one of the types of diseases that affects brain cells and causes memory loss, difficulty in thinking, ...

Diagnosing Microscopic Blood Samples for Early Detection of ...

Diagnosing Microscopic Blood Samples for Early Detection of Leukemia by Deep and Hybrid Learning Techniques · Ebrahim Mohammed Senan · Mukti E.

Deep and Hybrid Learning Techniques for Diagnosing Microscopic ...

All models attained exceptional results in the early detection of WBC diseases. Third, the hybrid technique was applied, consisting of a pair of blocks: the CNN ...

Deep and Hybrid Learning Technique for Early Detection of ...

Deep and Hybrid Learning Technique for Early Detection of Tuberculosis Based on X-ray Images Using Feature Fusion. Language: English; Authors: Fati, Suliman ...

Deep and Hybrid Learning Techniques for Diagnosing Microscopic ...

All models attained exceptional results in the early detection of WBC diseases. Third, the hybrid technique was applied, consisting of a pair of ...

Hybrid Model: Deep Learning Method for Early Detection of ...

This research presents a hybrid model based on a deep learning approach to detect Alzheimer's disease.

Deep and Hybrid Learning Technique for Early Detection of ... - OUCI

Then, the SVM algorithm is used for classifying features with high accuracy. This hybrid approach achieved superior results in diagnosing tuberculosis based on ...

A novel hybrid deep learning method for early detection of lung ...

This research article proposes a novel method for an early and accurate diagnosis called Cancer Cell Detection using Hybrid Neural Network (CCDC-HNN).

A Hybrid Deep Learning Framework for Early Diagnosis of ...

The medical imaging shows promising diagnosis on verifying it with the deep learning technique disease. This article proposed a hybrid model using transfer ...

Early detection of tuberculosis using hybrid feature descriptors and ...

The study proposed and simulated a deep learning prediction model for early TB diagnosis by combining deep features with hand-engineered features.

Ensemble of hybrid model based technique for early detecting of ...

Currently, machine learning (ML) and artificial neural networks (ANNs) are among the most promising approaches for developing automated computer ...

Early diagnosis of oral cancer using a hybrid arrangement of deep ...

The main contribution of the proposed DBN is its combination with a developed version of a metaheuristic technique, known as the Combined Group ...

Deep Learning and Rule-Based Hybrid Approach to Improve the Accur

Early diagnosis of the melanoma potentially increasing the chances of cure before cancer spreads. Recent development in Artificial Intelligence (AI) has wide ...

Detection of melanoma with hybrid learning method by removing ...

In this study, the hybrid learning method, that is, the machine learning method hybridizing with deep learning, was applied for the first time. •. In this study ...

Hybrid deep learning approach to improve classification of low ...

A hybrid learning approach is proposed that first trains a deep network on the training data, extracts features from the deep network, and then uses these ...

A deep hybrid learning pipeline for accurate diagnosis of ovarian ...

This novel Deep Hybrid Learning model, though derived from classical machine learning algorithms and standard CNN, showed a training and validation AUC score of ...

A Novel Early Detection and Prevention of Coronary Heart Disease ...

In comparison with classic machine learning approaches, proposed hybrid DL was found to achieve better performance in almost all studies that ...