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Lung Cancer Detection using VGG16 | Image Classification using Transfer Learning

  Рет қаралды 752

InsightsByRish

InsightsByRish

Ай бұрын

Hey everyone,
In this video I have implemented a project of 'Lung Caner Classification using pre-trained VGG16 model' which is a transfer learning technique.
VGG16 model has been trained on 'imagine' dataset which consist of 13 convolutional layers and 3 FC layers. Hence freezing the Conv Layers and building the FC layers.
Model Accuracy:
97%
Dataset:
www.kaggle.com...
Connect with me on Instagram:
/ insightsbyrish
IDE:
Google Colaboratory - colab.google
#imageclassification
#python
#deeplearning
#transferlearning
#cnn
Thanks for watching!
Please LIKE, SHARE and SUBSCRIBE if you enjoyed the content.

Пікірлер: 6
@RDFunFactory563
@RDFunFactory563 Ай бұрын
outstanding work, I learned so many things from your DL videos, can you please make a video on augmentation data with transfer learning using the image dataset you used in the lung cancer detection . thank you!
@InsightsByRish
@InsightsByRish Ай бұрын
Thanks for appreciation! I'll soon make that but on a different dataset. As the lung cancer one becomes too heavy for processing after augmentation.
@RDFunFactory563
@RDFunFactory563 Ай бұрын
@@InsightsByRish Okay mam, thank you, but please can you use the Histopathological images dataset, actually I am a graduate student researching cancer cell detection using Histopathological images. so I will get some help. thanks!
@jojo-t2o
@jojo-t2o 18 күн бұрын
@@InsightsByRish Hello there, Thanks for your video as its been very helpful. So I'm on similar model for different datasets but my current challenge is that my loss and accuracy are low. Can assistance with that.
@MuhammadEhtisham-cm5vr
@MuhammadEhtisham-cm5vr Ай бұрын
great work , i am learning alot from your videos , plz make deep learning projects related to cv and nlp , and plz try to train and test from a raw dataset , will be thankful ! keep up the good work
@InsightsByRish
@InsightsByRish Ай бұрын
Thank you! And yes, I'm making more such videos. Stay tuned!
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