Gastric Cancer Detection using Deep Learning
Gastric Cancer Detection and Classification Project developed using Python programming language and graphical user interface designed in tkinter. The machine learning model is trained on large dataset of images and user have to write training script to train model. After training user can test model on any images from dataset folder. User have to select images from detect button in home page and select image from dataset folder. Dataset contains 4 classes normal-cecum , normal-pylorus, normal z-line, polyp, etc. After detection model will classify that particular gastric disease and it will detect that particular portion in image.
Gastric Cancer Detection & Classification:-
There are rare research done on this topic. Basically we will create a model using machine learning algorithms (according to my current research, we can use mask-rcnn for detection of disease and VGG16 for classification) definitely we will customise those pre designed algorithms for our use.
A stomach cancer finder made with Python looks at pictures of the stomach to see if there’s cancer. It uses special tools to understand the pictures and figure out if there are any problems. The tools can recognize patterns in the pictures that might mean cancer is there. They’re like experts who can tell if something is wrong. The system needs to be easy to use and understand, like a simple game. It also needs to be checked to make sure it’s finding cancer correctly. This helps doctors find cancer early and treat it better.
Gastric Cancer Detection using Image Processing
Advantages of using Deep learning based Gastric Cancer Diagnosis System :-
– Early Detection: Finds cancer early.
– Accurate: Knows if there’s cancer or not.
– Saves Time: Helps doctors quickly.
– Reliable: Never makes mistakes.
– Better Treatment: Helps plan better treatment.
– Saves Money: Can make healthcare cheaper.
– Accessible: Can be used in many places.
Input : user will give endoscopic image of gastric area of human
Processing & output : our project will have 2 main layers
1. First the image will pass through detection model and check if the cancer is present in gastric area or not.
2. If it is present, model will highlight the area of cancer in image and display the type of cancer (normal-cecum, normal-pylorus, normal-z-line, polyp)
Technology to be used:-
Python, Tkinter framework for GUI, machine learning frameworks like TensorFlow, Keras, machine learning algorithms like VGG16, Mask-RCNN, resnet50, resnet101
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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