lung cancer is the leading cause of death among all the other cancer types. The detection and classification of pulmonary nodules is an open challenge. Hence we need to develop a new CAD system based on powerful feature map visualization techniques.
Lung cancer nodule detection using CT scan images
lung cancer is the leading cause of death among all the other cancer types. The detection and classification of pulmonary nodules is an open challenge. Hence we need to develop a new CAD system based on powerful feature map visualization techniques.
The objective is to develop a CNN-Based CAD system for nodule detection that increase sensitivity and maintain a low number of false positives in order to overcome the challenge of using automated system in daily medical practice.
A deep learning-based CAD system consists of five main stages:
1.Data Collection
2.Data Pre-processing
3.Image Segmentation
4.Candidate Nodules Detection
5.Benign-Malignant Classification
1.It will provide new and more robust technique for nodule detection that increase sensitivity and maintain a low number of false positives in order to overcome the challenge of using automated system in daily medical practice.
CAD system will be based on powerful feature map visualization techniques to better analyze CNN’s decision and transfer it to radiologists.
1.CNN-Based CAD system has the capability to greatly improve the global detection rate of lung nodules whenever an output is reported as wrong using iterative self-learning technique.
CNN-Based Computer-Aided Detection (CADe) system can extract valuable and relevant information on nodules, and can assist radiologists to establish the correct diagnosis.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Laptop | Equipment | 1 | 30000 | 30000 |
| Total in (Rs) | 30000 |
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