This project utilizes deep learning techniques, specifically Transfer Learning, to classify normal and cancerous lung tissues. By automating what once required years of medical expertise, computer vision now aids in the detection of cancerous cells. The model was developed using Google Colab, and the dataset was sourced from Kaggle.
YEAR
2023
ROLE
AI/ML Engineer
Data Scientist
SERVICES
AI/ML Frameworks & Modeling
Data Collection & Analysis
Transfer Learning
In convolutional neural networks (CNNs), convolutional layers play a key role in identifying and enhancing important features within images. These features, once learned (such as identifying edges, textures, or shapes), are often applicable across different tasks. Transfer Learning leverages this principle by reusing a model pre-trained on a large dataset (such as ImageNet) and applying it to a related task—like lung cancer detection.
By using a model that has already been fine-tuned over millions of images and across thousands of classes, we can adapt it to our specific needs with minimal adjustments. This technique significantly improves accuracy and reduces training time, as the convolutional layers have already learned general image features that can now be applied to our dataset.
Data Preparation
The dataset comprises 5000 images representing three types of lung conditions: normal, lung adenocarcinomas, and lung squamous cell carcinomas. The dataset has been expanded from an initial 250 images per class using data augmentation techniques, which were pre-applied to the images.
Libraries and Tools Used
Several powerful Python libraries are utilized to simplify and streamline complex tasks:
Pandas: Facilitates loading data into a 2D array format and includes numerous functions for quick and efficient data analysis.
Numpy: A fast-performing array library that enables handling large datasets and computations efficiently.
Matplotlib: Used for creating visualizations and plotting data to provide insight during the modeling process.
Sklearn: A suite of machine learning libraries that provide tools for data preprocessing, model building, and evaluation.
OpenCV: An open-source computer vision library designed for real-time image processing and manipulation.
TensorFlow: A robust open-source machine learning library that enables the implementation of deep learning models with ease.
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Lung Cancer Detection Tool
OVERVIEW
This project utilizes deep learning techniques, specifically Transfer Learning, to classify normal and cancerous lung tissues. By automating what once required years of medical expertise, computer vision now aids in the detection of cancerous cells. The model was developed using Google Colab, and the dataset was sourced from Kaggle.
YEAR
2023
ROLE
AI/ML Engineer
Data Scientist
SERVICES
AI/ML Frameworks & Modeling
Data Collection & Analysis
Transfer Learning
In convolutional neural networks (CNNs), convolutional layers play a key role in identifying and enhancing important features within images. These features, once learned (such as identifying edges, textures, or shapes), are often applicable across different tasks. Transfer Learning leverages this principle by reusing a model pre-trained on a large dataset (such as ImageNet) and applying it to a related task—like lung cancer detection.
By using a model that has already been fine-tuned over millions of images and across thousands of classes, we can adapt it to our specific needs with minimal adjustments. This technique significantly improves accuracy and reduces training time, as the convolutional layers have already learned general image features that can now be applied to our dataset.
Data Preparation
The dataset comprises 5000 images representing three types of lung conditions: normal, lung adenocarcinomas, and lung squamous cell carcinomas. The dataset has been expanded from an initial 250 images per class using data augmentation techniques, which were pre-applied to the images.
Libraries and Tools Used
Several powerful Python libraries are utilized to simplify and streamline complex tasks:
Pandas: Facilitates loading data into a 2D array format and includes numerous functions for quick and efficient data analysis.
Numpy: A fast-performing array library that enables handling large datasets and computations efficiently.
Matplotlib: Used for creating visualizations and plotting data to provide insight during the modeling process.
Sklearn: A suite of machine learning libraries that provide tools for data preprocessing, model building, and evaluation.
OpenCV: An open-source computer vision library designed for real-time image processing and manipulation.
TensorFlow: A robust open-source machine learning library that enables the implementation of deep learning models with ease.
Smooth Scroll
This will hide itself!
This will hide itself!
Lung Cancer Detection Tool
OVERVIEW
This project utilizes deep learning techniques, specifically Transfer Learning, to classify normal and cancerous lung tissues. By automating what once required years of medical expertise, computer vision now aids in the detection of cancerous cells. The model was developed using Google Colab, and the dataset was sourced from Kaggle.
YEAR
2023
ROLE
AI/ML Engineer
Data Scientist
SERVICES
AI/ML Frameworks & Modeling
Data Collection & Analysis
Transfer Learning
In convolutional neural networks (CNNs), convolutional layers play a key role in identifying and enhancing important features within images. These features, once learned (such as identifying edges, textures, or shapes), are often applicable across different tasks. Transfer Learning leverages this principle by reusing a model pre-trained on a large dataset (such as ImageNet) and applying it to a related task—like lung cancer detection.
By using a model that has already been fine-tuned over millions of images and across thousands of classes, we can adapt it to our specific needs with minimal adjustments. This technique significantly improves accuracy and reduces training time, as the convolutional layers have already learned general image features that can now be applied to our dataset.
Data Preparation
The dataset comprises 5000 images representing three types of lung conditions: normal, lung adenocarcinomas, and lung squamous cell carcinomas. The dataset has been expanded from an initial 250 images per class using data augmentation techniques, which were pre-applied to the images.
Libraries and Tools Used
Several powerful Python libraries are utilized to simplify and streamline complex tasks:
Pandas: Facilitates loading data into a 2D array format and includes numerous functions for quick and efficient data analysis.
Numpy: A fast-performing array library that enables handling large datasets and computations efficiently.
Matplotlib: Used for creating visualizations and plotting data to provide insight during the modeling process.
Sklearn: A suite of machine learning libraries that provide tools for data preprocessing, model building, and evaluation.
OpenCV: An open-source computer vision library designed for real-time image processing and manipulation.
TensorFlow: A robust open-source machine learning library that enables the implementation of deep learning models with ease.
Smooth Scroll
This will hide itself!
This will hide itself!