Deep Learning-Based Multi-Class Classification of Breast Digital Pathology Images

Diabetes mellitus is a form of diabetes with secondary microvascular complication leading to renal dysfunction and retinal loss also termed as diabetic retinopathy. Retinopathy is grave form of retinal disease. It is the leading cause of blindness in the world. Blockage of tiny minute retinal blood vessels due to the high blood sugar level is the reason why retinopathy leads to blindness or loss of vision. This study serves the purpose of deep learning-based diagnosis of Diabetic retinopathy using the fundus imaging of the eye. In this study architectures such as VGG 16 and VGG 19 are deployed in order to classify the images into 5 categories. The performance of the two models were compared. The highest accuracy is 77.67% when using the VGG 16 pre-trained model.

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Deep Learning-based Method for Multi-Class Classification of Oil Palm Planted Area on Plant Ages Using Ikonos Panchromatic Imagery

International Journal on Advanced Science Engineering and Information Technology ◽

10.18517/ijaseit.10.6.12030 ◽

2020 ◽

Vol 10 (6) ◽

pp. 2200

Author(s):

Soffiana Agustin ◽

Handayani Tjandrasa ◽

R.V. Hari Ginardi

Keyword(s):

Deep Learning ◽

Oil Palm ◽

Multi Class Classification

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A Deep Learning Model For Dimension Reduction And Multi-Class Classification Of Gene Expression Data

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i8.671676 ◽

2018 ◽

Vol 6 (8) ◽

pp. 671-676

Author(s):

Aradhita Mukherjee ◽

Dibyendu Bikash Seal

Keyword(s):

Gene Expression ◽

Deep Learning ◽

Dimension Reduction ◽

Gene Expression Data ◽

Learning Model ◽

Expression Data ◽

Multi Class Classification ◽

Deep Learning Model

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Analysis of Multi-class Classification of EEG Signals Using Deep Learning

Pattern Recognition and Artificial Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-030-59830-3_18 ◽

2020 ◽

pp. 203-217

Author(s):

Dipayan Das ◽

Tamal Chowdhury ◽

Umapada Pal

Keyword(s):

Deep Learning ◽

Eeg Signals ◽

Multi Class Classification

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A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer’s Disease Stages Using Resting-State fMRI and Residual Neural Networks

Journal of Medical Systems ◽

10.1007/s10916-019-1475-2 ◽

2019 ◽

Vol 44 (2) ◽

Cited By ~ 8

Author(s):

Farheen Ramzan ◽

Muhammad Usman Ghani Khan ◽

Asim Rehmat ◽

Sajid Iqbal ◽

Tanzila Saba ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Neural Networks ◽

Deep Learning ◽

Resting State ◽

Resting State Fmri ◽

Learning Approach ◽

Automated Diagnosis ◽

Multi Class Classification ◽

Disease Stages

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Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection

BioMedical Engineering OnLine ◽

10.1186/s12938-020-00831-x ◽

2020 ◽

Vol 19 (1) ◽

Author(s):

Lal Hussain ◽

Tony Nguyen ◽

Haifang Li ◽

Adeel A. Abbasi ◽

Kashif J. Lone ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Bacterial Pneumonia ◽

Lung Infection ◽

Morphological Features ◽

Viral Pneumonia ◽

Diagnostic Efficiency ◽

Portable Chest ◽

Multi Class Classification

Abstract Background The large volume and suboptimal image quality of portable chest X-rays (CXRs) as a result of the COVID-19 pandemic could post significant challenges for radiologists and frontline physicians. Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs. Purpose The study aimed at developing an AI imaging analysis tool to classify COVID-19 lung infection based on portable CXRs. Materials and methods Public datasets of COVID-19 (N = 130), bacterial pneumonia (N = 145), non-COVID-19 viral pneumonia (N = 145), and normal (N = 138) CXRs were analyzed. Texture and morphological features were extracted. Five supervised machine-learning AI algorithms were used to classify COVID-19 from other conditions. Two-class and multi-class classification were performed. Statistical analysis was done using unpaired two-tailed t tests with unequal variance between groups. Performance of classification models used the receiver-operating characteristic (ROC) curve analysis. Results For the two-class classification, the accuracy, sensitivity and specificity were, respectively, 100%, 100%, and 100% for COVID-19 vs normal; 96.34%, 95.35% and 97.44% for COVID-19 vs bacterial pneumonia; and 97.56%, 97.44% and 97.67% for COVID-19 vs non-COVID-19 viral pneumonia. For the multi-class classification, the combined accuracy and AUC were 79.52% and 0.87, respectively. Conclusion AI classification of texture and morphological features of portable CXRs accurately distinguishes COVID-19 lung infection in patients in multi-class datasets. Deep-learning methods have the potential to improve diagnostic efficiency and accuracy for portable CXRs.

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