Digital Forensics Use Case for Glaucoma Detection Using Transfer Learning Based on Deep Convolutional Neural Networks

Table 2

Summary of related work on image classification using ML and DL approaches publishing year, primary contribution attributes, and approach name.

Ref no.	Key contribution	Approach
Sarki et al. [19]	Fundus images are used to detect eye disease of the diabetic patient using a deep learning approach	Deep learning models
Hameed et al. [30]	DME classification	Decision tree
Yu and Xiao 2017 [20]	Retinopathy of diabetic patients for detecting exudate	CNN
Chudzik et al. [21]	Interleaved freezing of deep learning method for microaneurysm detection	Transfer learning and layer freezing
Hatanaka et al. [22]	Retinal images are used to automatic microaneurysms detect using deep convolution neural network	DCNN
Dai et al. [23]	•Multi-sieving deep learning is used to detect retinal microaneurysm for clinical report	CNN
Saba et al. [24]	Glaucoma detection using fundus image	A mixture of ML methods
Fourcade et al. [25]	Image analysis for medical purposes using deep learning	CNN
Faes et al. [26]	Medical image classification using deep learning model design	Google cloud AutoML
Katzmann et al. [27]	Medical small-sized image data classification using RF algorithm	Random forest classifiers and deep ensembles
Smaida and Yaroshchak [28]	Deep learning convolutional network based on keras and tensor flow using python for image classification	DCNN
Hameed et al. [29]	Eye diseases classification	Back propagation with parabola learning rate