Cognitive Brain Tumour Segmentation Using Varying Window Architecture of Cascade Convolutional Neural Network

2021 ◽  
Vol 11 (4) ◽  
pp. 21-29
Author(s):  
Mukesh Kumar Chandrakar ◽  
Anup Mishra
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Brain Tumour ◽  
Feature Space ◽  
Training Model ◽  
Research Area ◽  
Training Data ◽  
Tumour Segmentation ◽  
Specificity And Sensitivity ◽  
Brain Tumour Segmentation

Brain tumour segmentation is a growing research area in cognitive science and brain computing that helps the clinicians to plan the treatment as per the severity of the tumour cells or region. Accurate brain tumor detection requires measuring the volume, shape, boundaries, and other features. Deep learning is used to measure the characteristics without human intervention. The proper parameter setting and evaluation play a major role. Keeping this in mind, this paper focuses on varying window cascade architecture of convolutional neural network for brain tumour segmentation. The cognitive brain tumour computing is associated with the model using cognition concept for training data. The mixing of training data of different types of tumour images is applied to the model that ensures effective training. The feature space and training model improve the performance. The proposed architecture results in improvement in dice similarity, specificity, and sensitivity. The approach with improved performance is also compared with the existing approaches on the same dataset.

scholarly journals Knowledge-driven deep neural network models for brain tumour segmentation

2020 ◽  
Vol 1662 ◽  
pp. 012010
Author(s):  
F Colecchia ◽  
J K Ruffle ◽  
G C Pombo ◽  
R Gray ◽  
H Hyare ◽  
...  
Keyword(s):  
Neural Network ◽  
Brain Tumour ◽  
Deep Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Tumour Segmentation ◽  
Brain Tumour Segmentation

scholarly journals Indonesian news classification using convolutional neural network

2020 ◽  
Vol 19 (2) ◽  
pp. 1000
Author(s):  
Muhammad Ali Ramdhani ◽  
Dian Sa’adillah Maylawati ◽  
Teddy Mantoro
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Research Area ◽  
Training Data ◽  
Text Data ◽  
Testing Data ◽  
Headline News ◽  
Language Characteristics ◽  
Area Data

<span>Every language has unique characteristics, structures, and grammar. Thus, different styles will have different processes and result in processed in Natural Language Processing (NLP) research area. In the current NLP research area, Data Mining (DM) or Machine Learning (ML) technique is popular, especially for Deep Learning (DL) method. This research aims to classify text data in the Indonesian language using Convolutional Neural Network (CNN) as one of the DL algorithms. The CNN algorithm used modified following the Indonesian language characteristics. Thereby, in the text pre-processing phase, stopword removal and stemming are particularly suitable for the Indonesian language. The experiment conducted using 472 Indonesian News text data from various sources with four categories: ‘hiburan’ (entertainment), ‘olahraga’ (sport), ‘tajuk utama’ (headline news), and ‘teknologi’ (technology). Based on the experiment and evaluation using 377 training data and 95 testing data, producing five models with ten epoch for each model, CNN has the best percentage of accuracy around 90,74% and loss value around 29,05% for 300 hidden layers in classifying the Indonesian News data.</span>

scholarly journals Implementasi Deep Learning Untuk Klasifikasi Citra Undertone Menggunakan Algoritma Convolutional Neural Network

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Rizka Fayyadhila ◽  
Apri Junaidi ◽  
Novian Adi Prasetyo
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Skin Color ◽  
Color Image ◽  
Feature Learning ◽  
Training Model ◽  
Training Data ◽  
Hair Color ◽  
Image Size ◽  
Learning Stage

The beauty of Indonesian women is distinguished by skin color, facial structure, hair color and body posture. For women today trying to look beautiful is a must. The way to make yourself look beautiful can be tricked by using make-up. But it's not that easy to use make-up because the type of make-up is differentiated based on the basic skin color, this is the problem for women in using make-up. Undertone is the basic color of the skin, there are three types of undertones, namely warm, cool and neutral. By knowing the type of undertone, it will make it easier for women to use make-up, namely to determine the appropriate shade based on the type of undertone. For this reason, a modeling of undertone image classification was made using the Convolutional Neural Network algorithm. This algorithm is claimed to be the best algorithm for solving object recognition and detection problems. The wrist vein color image dataset is required. The dataset used is 30 data per class, then preprocessing is carried out by homogenizing the image size to 64x64 pixels, then augmentation is carried out on each image by rotating and zooming. At this stage, the dataset will be divided into 3000 images which are divided into 80% training data and 20% testing data. Then it is processed through the convolution and pooling process at the feature learning stage, then the fully connected layer and classification stage where the feature learning results will be used for the classification process based on subclasses. Produces accuracy and training model values ​​reaching 98% with a loss value of 0.0214 and for accuracy from data validation it reaches 99% with a loss value of 0.0239 with model testing results of 99.5%.

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