Introduction

Analysis of medical images aids in redressing numerous clinical problems with the examination of images that are generated in the clinic. In the current era, the prospects derived from the deep learning concepts are immense owing to the application of advanced computer vision concepts. Especially, the medical image analyses seem to get much benefited from the deep learning concepts. The incorporation of deep learning and Artificial Intelligence (AI) has raised gradually in the recent few years.

In numerous investigations, Convolutional Neural Networks (CNNs) are chosen by many of the investigators since it is generally recognized as one of the most efficient tools used in the analysis of images and related organized data. CNN frameworks inherently get to know the hierarchy and space by deploying convolution, backpropagation, link, and pooling layers. Investigations based on CNN often focus on image classification, image pre-processing, and assessing several uses of CNN approaches in healthcare imaging. The incorporation of CNNs into the analyses of medical images can assure certain prospects, namely, streamlined workflow efficiency, improved diagnostic preciseness, and extend access to professional standard image investigation. Therefore, the incorporation of CNNs contributes towards the final aim of offering extended enhancements in healthcare and patient results.

In this write-up, we will discuss key whereabouts of CNN along with a few related final year engineer projects.

Introduction to AI

Artificial intelligence (AI) is a field of computer science. Its objectives include the creation of frameworks with a potential of executing works (for instance, decision-making, speech identification, language translation, and visual understanding) that generally need the smartness of human beings.

With the prevalence of numerous AI tools, the evolution in many sectors (for example, medical care) have become possible. Approaches based on AI are being utilized to draw favorable outcomes in decision-making, diagnostic, and predictive capabilities. Across the healthcare sector, the excellence of AI is realized in numerous uses, namely, patient result prediction tools; decision support tools like transplantation-assisting organ allotment; and pathology and radiology investigation. 

What is CNN?

Convolutional Neural Networks (CNNs) are a kind of deep neural network that can empower processing and analyzing of visual info like videos and images. AI has predominantly found to be used in the image analysis, which has already proved its benefits. CNNs are the part of AI framework that helps to excel the domain of medical image analysis. These CNNs play a critical part, especially in the computer vision, a domain that could facilitate the empowering of machines to sight and decode visual information. Its utilization makes it possible to raise the accessibility, speed, and accuracy with regard to analysis and interpretation of medical images.

These networks comprise several layers which uses convolution processes for identifying attributes and patterns from the input data. Kernels or filters glide across the image input, executing element-wise summations and multiplications, producing attribute maps for empowering highlighting of specific image patterns. Its training operation indulges supply of the tagged data via the network. The network has the potential to modify its intrinsic variables like biases and weights for minimizing the dissimilarity between the actual tags and its corresponding predictions. This operation is empowered with a loss function, quantifying the optimization approaches like Stochastic Gradient Descent (SGD) that is responsible for updating of variables in the network and prediction error.

Operations in Advanced Medical Image Analysis

Image processing is the evolving idea in the domain of healthcare. It imparts considerable info pertaining to the decision-making. Numerous types of operations are done in the healthcare domain prior to the generation of outputs. Medical image is provided as the input onto the deep learning paradigms (for instance, CNN) and subsequently it is divided into portions for focusing upon the significant region. Afterwards, these divided portions are utilized to extract important info by using information recovering approaches. Subsequently, needed attributes are acquired with nil noise by deploying noise elimination approaches. The classification of the acquired data takes place with the help of classifiers and then the predictions are made according to the classification made. These procedures are done for each iteration executed in deep learning.

Benefits of CNN in Medical Image Analysis

The benefits of CNN in medical image analysis are much immense, a few of which are as follows:

• With CNNs, the access to professional standard image investigation becomes possible even in the underdeveloped or remote regions. Thus, both the patients and physicians could be benefited.

• The deployment of CNNs have facilitated the expedition the pace of image analysis done by physicians. Thus, the workflow effectiveness is enhanced and could also give rise to reduced TAT.

• CNNs have revealed its capability to fulfill or exceed professional evaluation, contributing to much accurate diagnosis operations and enhanced patient results.

Also Read: Image Processing Projects

Top 30 Final Year Engineer Projects

Now, let us see top 30 final year engineer projects concentrating specifically on the CNNs or concepts of deep learning in medical image analysis. All the listed final year engineer projects cover various significant genres like deep learning, image processing, image classification, MATLAB, CNNs, radiology, pathology, etc. in the medical field.

• Deep Convolution Neural Network for Big Data Medical Image Classification 

• Research on Medical Image Classification Based on Machine Learning

• MRI Breast Tumor Segmentation Using Different Encoder and Decoder Using CNN Architecture

• A Novel End-to-End Hybrid Network for Alzheimer’s Disease Detection Using 3D CNN and 3D CLSTM

• Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification 

• Abnormality Detection in Medical Images with Faster-RCNN Using Matlab 

• Diagnosis Support Model of Cardiomegaly based on CNN using ResNet and Explainable Feature Map

• Bone Fracture Classification using SVM and CNN

• Skin Cancer Classification Using DL Based on CNN

• Classification of Malaria-Infected Cells using Convolutional Neural Networks 

• 3D APA-Net: 3D Adversarial Pyramid Anisotropic Convolutional Network for Prostate Segmentation in MR Images

• Deep Learning Applications in Medical Image Analysis

• Medical Image segmentation using UNet Architecture

• Discovering Knee Osteoarthritis Using CNN enhanced With Alex Net

• Colorectal Cancer Detection by using Deep Learning

• Detection of Cardiovascular Diseases in ECG Images using Machine Learning and Deep Learning Methods

• Detection of ALL Disease

• Disease Classification of Liver and Lungs Using Deep learning

• Parkinson’s Disease Detection Using Deep Learning Techniques

• Brain Stroke Detection using CT or MRI Scan

• A Computational Method for Learning Disease

• Detection of Diabetes Mellitus with Deep Learning and Data Augmentation Techniques on Foot Thermography

• Detection of Chronic Kidney Disease Using Deep Learning

• Bone Deformity Identification Using Machine Learning

• Detection of Knee Orthopedic X-ray Images using Deep Learning

• Cancer Detection using Machine Learning( Cervical, Breast and Lung Cancer) 

• Monkey Pox Disease Detecting System Using Deep Learning

• Liver Tumor detection Using Deep Learning Techniques

• Segmentation of Retinal Lesions in Fundus Images using Auto Encoder-Decoder Architecture

Must Read: Deep Learning Final Year Projects

Summary

CNNs have completely reshaped the domain of medical image analysis by favorably contributing to the precise image segmentation, disease identification, and image classification. These CNNS have become indispensable for both the medical investigators and professionals. Because of the advances made in the medical image analysis, it will be possible to address issues like privacy problems, interpretability, and data insufficiency.