10 deep learning projects for final year

What is Deep Learning?

Deep learning is a way of teaching computers to learn from data, just like humans do. It uses artificial neural networks, which are like simplified models of the brain, to process large amounts of data and find patterns or features in it. For example, deep learning can help computers recognize images, understand speech, translate languages, and play games. Deep learning is a type of machine learning, which is a branch of artificial intelligence. Deep learning is different from traditional machine learning because it can learn from unstructured data, such as text and images, without needing human experts to label or organize it. Deep learning can also learn from multiple layers of data, which makes it more powerful and accurate.

Difference between Deep Learning and Machine Learning

Aspect	Machine Learning	Deep Learning
Definition	Type of artificial intelligence which focus on algorithms and learn through data	Type of machine learning which use artificial neural networks and learn through various data layers
Complexity of Models	It use simple models like random forest, decision tree, liner regression etc.	It use complex multi-layer models to learn feature hierarchies.
Training Data Size	It works well on smaller datasets.	It requires large amount of data for effective learning
Computation Requirement	Require less computational power and resource	Require significant computational power and resources
Feature Representation	Require manual engineering and feature extraction	Automatically extract features from raw data
Examples	Linear Regression, Support vector machine, decision tree, random forest,	CNN and RNN

Deep Learning Projects for Final Year

In AI projects, the most critical thing is accuracy; deep learning provides better accuracy than machine learning. So you should select deep learning projects for your final year. These projects will enhance your AI skills and help you get good grades in your final year. The final year project is complex, from idea selection to documentation and code. It is all very complex, but don’t worry; I have compiled ten deep-learning projects to help you in your final year project. Don’t take tension and chill.

1. Breast Cancer Detection using Deep Learning

Breast cancer is the most deadly disease among women worldwide. It is more dominant than lung cancer. According to statistics, 2.3 Million new cases were reported in 2020, which resulted in 685000 deaths. Cancer starts with cells in the breast tissue and then multiply, affecting the healthy tissues.

Medical images can be obtained from multiple technologies like MRI, ultrasound, thermography, and Histopathology. Histopathology is considered the best method to get pictures of breast cancer cells. From there, you can collect images to train deep-learning models.

Deep learning models have the benefit of requiring no prior knowledge of the data. Deep Learning needs the input data to be in the correct format, and the network parameters relevant to the problem must be given as we have collected data images. First of all, we will apply data preprocessing, and then we will split the data into training and testing tests. Next, we will build the CNN model, and after that, we will train the model and evaluate it.

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2. Crop Disease Detection using deep learning

In recent years, climate change and lack of immunity in crops have increased the growth of diseases in crops. This causes a decrement in crop production and results in financial loss for farmers. Agriculture is the backbone of the country. A loss in the agriculture sector means a loss in the overall economy. Due to the rapidly growing variety of diseases and limited knowledge of farmers, treatment of these crop diseases has become challenging. To solve this, technology comes in. Computer vision integrated with deep learning provides a better solution to solve this problem.
Every plant has some texture and attributes that can help undetected diseases in crops. However, accuracy is critical to detecting the right disease in crops.
First, look at how this system works. The user will input an image of Crop. Your deep learning model will check it and give output according to crops’ diseases. To develop this system, you will pre-process data and extract features. Then, we will apply the CNN model and then get a prediction from it.

3. Real-Time Image Animation

The social media industry is booming, and online presence is vital. But for online presence, you need engaging content. Trends are changing day by day. A Facebook post trend changed in reels due to Tiktok and is now evolving toward animation. Animations are beautiful and engaging.
Traditional methods of adding animation are complex and need frame-by-frame changing. It consumes a lot of time and money. But now AI is empowering this feature. You can create real-time image detection using deep learning.
The user will give an image as input, and your model will add animation to it and convert it to video. Many tools exist to create animation, from pictures to animated videos. However, accuracy is still a challenge for all. If you can make this final year project, you will get two benefits. Firstly, you will get good grades; secondly, if you launch this in website format, you can earn money. However, it is a complex project and requires many iterations to improve it.

4. Human Pose Detection

This is a computer vision project which detect the joints and body parts such as the head, knees, wrists, and elbows, in an in picture or video. This task is used in a number of applications, including sports analytics and surveillance systems. Estimating human position is significant because it allows machines to interact with the human environment by recognizing human body poses, movements, and behaviors.

5. Language Translator Using Deep Learning

Language translator using deep learning aims to create a system that automatically translate the text from one language to another. This project use special type of neural network called a sequence to sequence model. This model consist of two parts an encoder and decoder.When a user enters text as input, the encoder receives it, converts it to vector representation, and then decodes it into output text.Additionally, an attention mechanism is used in this deep learning project to assist the decoder in focusing on relevant portions of the input.

6. Hand Gesture Recognition System

The next one is the hand gesture recognition system. This system enables human-computer interaction by taking hand gestures as input. It can be helpful for speech-impaired people, as well as for gaming, virtual reality, and other applications. There are three critical parts to a typical hand gesture recognition system: getting data, separating gestures into groups, and classifying gestures. Getting data means using cameras or sensors to take pictures or videos of hand movements. To do gesture segmentation, you must separate the hand area from the background and pull out traits like shape, colour, or motion.

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7. Skin Cancer Detection

Skin cancer is one of the most harmful cancers, which may lead to death. Skin is an essential part of the body as it covers the whole body. If there is a defect in one place on the skin, it will disturb other body parts. Early diagnosis of skin disease is essential for treatment. Traditional methods are not enough to diagnose skin disease. But AI is here to help us. Deep learning algorithms can help us in the early diagnosis of skin cancer with just an image input. The user will enter the image, and the Deep Learning model will predict if a person has skin cancer.

8. Face Mask Detection

This project was trending in 2020 due to the COVID-19 face mask detection system, which detects if a person is wearing a mask. The function happens in two steps: face detection and the mask detection section of the mask. First, it will crop an image of the face and then check if a person is wearing a mask. This system can be used to enforce masks in public. First, to create this project, get a dataset from Kaggle, pre-process data, extract features, and train your model. At the end, test and evaluate your mode.

9. Object Detection System

Object recognition systems are at the heart of computer vision. They change numerous industries by detecting and identifying things in digital photos and videos. Finding and recognizing multiple objects in real life makes traditional computer vision jobs hard. The Object Detection System finds things quickly and correctly. Object recognition is helpful for many essential things, like self-driving cars, surveillance systems, medical imaging, and retail analytics. Images or video frames are introduced into the system, which uses complex algorithms to find and identify items.

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10. Fake news Detection system

Fake news and disinformation pose a challenge to society and the news industry. Fake news is simple to share these days. You can create a phoney news detecting system to address this critical issue. This system will employ AI algorithms to determine whether the news is fake. Twitter(X) is also working on this technique to ensure their tweets are accurate and reliable. To create this system, get a dataset from Kaggle and run the machine learning algorithm.