Azman Nurhakim's Projects
Classification of the Hyperspectral Image Indian Pines with Convolutional Neural Network
My FYP on using Deep Learning to identify brain based medical conditions in children
Image Classification using Keras as well as Tensorflow.
Building a powerful image classifier, using only very few training examples --just a few hundred or thousand pictures from each class you want to be able to recognize.
Keras- Multi Label Image Classification
an image repo of city councils logo
Tumour is formed in human body by abnormal cell multiplication in the tissue. Early detection of tumors and classifying them to Benign and malignant tumours is important in order to prevent its further growth. MRI (Magnetic Resonance Imaging) is a medical imaging technique used by radiologists to study and analyse medical images. Doing critical analysis manually can create unnecessary delay and also the accuracy for the same will be very less due to human errors. The main objective of this project is to apply machine learning techniques to make systems capable enough to perform such critical analysis faster with higher accuracy and efficiency levels. This research work is been done on te existing architecture of convolution neural network which can identify the tumour from MRI image. The Convolution Neural Network was implemented using Keras and TensorFlow, accelerated by NVIDIA Tesla K40 GPU. Using REMBRANDT as the dataset for implementation, the Classification accuracy accuired for AlexNet and ZFNet are 63.56% and 84.42% respectively.
My deep learning project during my intern at MIMOS BERHAD
Implementation of Deep Neural Networks in Keras and Tensorflow to classify motor imagery tasks using EEG data
CNN for multi-class image recognition in tensorflow