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Abstractppt

The document presents a project focused on detecting lung cancer from CT images using SVM classification and comparing patient survival rates with 3D CNN. It outlines the process of image pre-processing, including data gathering, cleaning, segmentation, and analytics, all utilizing deep learning and image processing techniques. Additionally, it specifies the software and hardware requirements necessary for executing the project.

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9 views7 pages

Abstractppt

The document presents a project focused on detecting lung cancer from CT images using SVM classification and comparing patient survival rates with 3D CNN. It outlines the process of image pre-processing, including data gathering, cleaning, segmentation, and analytics, all utilizing deep learning and image processing techniques. Additionally, it specifies the software and hardware requirements necessary for executing the project.

Uploaded by

yopekah179
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as PDF, TXT or read online on Scribd
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Abstract Review

Under Esteemed Guidance of


Mr. Chandra Mouli

Submitted by :
M. Bhuvana Poojitha
20KT1A0537
Title 1 : Detection of Lung cancer from CT
image using SVM classification and
compare the survival rate of patients using
3D Convolutional neural network (3D CNN)
on lung nodules data set.
Problem statement :
Efficient method of detecting Lung Cancer diagnosis.
ABSTRACT
In this technological era, we are determined to computerize everything using
Artificial intelligence and Machine Learning. The medical industry is no
exception. As we previously had known the wonders of AI and data
analytics technologies has done in the medical industry. In this project, we
are pre-processing the medical picture or a CT scan image. The image is
segmented into small pictures using the method called CLAHE Equalization.
Once, the region of interest(ROI) in the image is identified which is related
to the lung cancer cell, the segmentation process proceeds to the next pixel
of the CT image. The process includes – data gathering , data cleaning,
segmentation, data analytics, and conclusion of the problem. The mentioned
procedures are all based on the deep learning, data analytics and image
processing techniques.
Software requirements :
1. Operating System: Ubuntu 20.04 LTS or higher, or Windows 10 with WSL
2.
2. Python: Python 3.8 or higher, with support for numpy, pandas, matplotlib,
scikit-learn, and tensorflow-gpu.
3. Deep Learning Framework: TensorFlow 2.6 or higher, with support for
Keras and GPU acceleration.
4. Image Processing Library: OpenCV 4.5 or higher, with support for image
processing and analysis.
5. Data Analysis Tools: Jupyter Notebook or Google Colab for data exploration
and visualization
Hardware Requirements:

1. CPU: A modern multi-core processor with at least 8


cores, such as an Intel i7 or AMD Ryzen 7.
2. RAM: At least 32 GB of memory, preferably 64 GB or
more, to handle large datasets and multi-tasking.
3. GPU: A powerful graphics card with at least 8 GB of
memory, preferably 11 GB or more, such as an
NVIDIA RTX 3080 or higher, to accelerate deep
learning computations.
4. Storage: A fast solid-state drive (SSD) with at least 1
TB of capacity, preferably 2 TB or more, to store
datasets and trained models.
Thank you

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