Poineering in Dermatology based Disease Prediction Using Deep learning
The project built with the acquisition of a diverse and labeled
dataset of dermatological images. This dataset recognized for its
quality and relevance, formed the cornerstone of my project. Data
Augmentation techniques were applied to enhance dataset diversity.
The modal was created using a Convolutional Neural Network (CNN),
I constructed this system using state-of-the-art technologies, with
TensorFlow and Keras serving as the foundation. I acquired a diverse
and labeled dataset of dermatological images, ensuring quality and
relevance. Employing data augmentation techniques enhanced the
model's diversity. The powerful InceptionV3 pre-trained model became
my key instrument. Fine-tuning improved prediction accuracy, and
rigorous validation ensured reliability.
Model Can able to predict a wide range of skin diseases, making it a
valuable tool for dermatological Health. In-Depth Information –
Beyond diagnosis, our model offers detailed explanations that
encompass the causes, symptoms, risk factors, and treatment plan
for an identified skin condition
Programming Language: Python - well-known for its suitability in deep learning, making it fit for working with Neural networks such as CNNs.
Deep Learning Framework: Tensorflow and Keras.
Data Management: Pandas and NumPy for managing and manipulating data.
Image Processing: OpenCV - essential for working with dermatology images.