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

Abstract

In parallel with the development of various emerging fields such as computer vision and related technologies, e.g., iris identification and glaucoma detection, criminals are developing their methods. It is the foremost reason for the blindness of human beings that affects the eye’s optic nerve. Fundus photography is carried out to examine this eye disease. Medical experts evaluate fundus photographs, which is a time-consuming visual inspection. Most current systems for automated glaucoma detection in fundus images rely on segmentation-based features nuanced by the underlying segmentation methods. Convolutional neural networks (CNNs) are powerful tools for solving image classification tasks, as they can learn highly discriminative features from raw pixel intensities. However, their applicability to medical image analysis is limited by the nonavailability of large sets of annotated data required for training. In this work, we aim to accelerate this process using a computer-aided diagnosis of this severe disease with the help of transfer learning based on deep convolutional neural networks. We have suggested the Inception V-3 approach for image classification based on convolution neural networks. Our developed model has the potential to address this CNN model’s problem of classification accuracy, and with imaging data, our proposed method outperforms recent state-of-the-art approaches. The case study for digital forensics is an essential component of emerging technologies, and hence glaucoma detection plays a vital role in it.

Advancements in intrusion detection: A lightweight hybrid RNN-RF model

“…The suggested method is evaluated using the Bot-IoT dataset, and the outcomes demonstrate notable gains in detection performance over current techniques [45,46]. The suggested method is a promising addition to the field of IoT security since it can be expanded to improve the security of additional IoT applications [47][48][49][50][51][52][53].…”

A Robust Machine Learning Model for Diabetic Retinopathy Classification

“…The hands-on engineering methods, such as ensemble learning, deep ensemble networks, and end-to-end learning-based approaches, extract features using advanced or traditional methods [1,8,9]. Ensemble learning algorithms can be used for designing ensembles of neural networks or ensemble machine learning (EL).…”

Fault Early Warning Based on Improved Deep Neural Network of Auto-Encoder

“…e big data bring new opportunities and challenges to the health monitoring and fault diagnosis of mechanical equipment [7]. In recent years, deep learning has made a breakthrough in big data analysis in the fields of speech recognition and image recognition, and deep learning theory has been applied in the fields of mechanical fault diagnosis and health monitoring [8][9][10][11], but the diagnosis and identification of faults are still in the preliminary exploration stage [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. A deep neural network of auto-encoder (DAE) can extract fault features from noise signals and can be well combined with a sample enhancement method to deal with small sample problems [30][31][32][33][34][35][36][37].…”