A Multiclass Deep Convolutional Neural Network Classifier

8 hours ago A Multiclass Deep Convolutional Neural Network Classifier for Detection of Common Rice Plant Anomalies Ronnel R. Atole Institute of Information Technology Partido State University San Juan Bautista, Goa, Camarines Sur, 4422 Philippines Daechul Park Dept. of Computer, Comm. and Unmanned Technology Hannam University

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3 hours ago Abstract: This study examines the use of deep convolutional neural network in the classification of rice plants according to health status based on images of its leaves. A three-class classifier was implemented representing normal, unhealthy, and snail-infested plants via transfer learning from an AlexNet deep network.

Author: Ronnel R. Atole, Daechul Park
Publish Year: 2018

Category: Random forest classifier multiclass Preview  Show details

3 hours ago A Multiclass Deep Convolutional Neural Network Classifier for Detection of Common Rice Plant Anomalies Ronnel R. Atole Institute of Information …

Estimated Reading Time: 5 mins

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2 hours ago In this article we will try to classify natural scenes around the world. We would be using the dataset provided by Intel, in one of its image classification challenge. 6 Classes — ‘buildings’ (0),
1. The Classification Net consists of two layers — The Flatten Layer and The Fully Connected Layers. The Flatten layer is used to convert the 2D output array from Pooling Layer or Convolutional layer to 1D array (Flattening the input) before feeding it to the fully connected layers. The fully connected layers are a network of serially connected dense ...

Estimated Reading Time: 8 mins
Published: Jul 11, 2020

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9 hours ago Multi-class classification example with Convolutional Neural Network in Keras and Tensorflow In the previous articles, we have looked at a regression problem and a binary classification problem. Let's now look at another common supervised learning …

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1 hours ago In this work, we have developed a Deep Convolutional Neural Network (CNN) to segment and classify the various types of breast abnormalities, such as calcifications, masses, asymmetry and carcinomas, unlike existing research work, which mainly classified the cancer into benign and malignant, leading to improved disease management.

Author: Maleika Heenaye-Mamode Khan, Nazmeen Boodoo-Jahangeer, Wasiimah Dullull, Shaista Nathire, Xiaohong G...
Publish Year: 2021
Author: Imran Iqbal, Muhammad Younus, Khuram Walayat, Mohib Ullah Kakar, Jinwen Ma
Publish Year: 2021

Category: Breast Cancer Health Preview  Show details

4 hours ago A Multiclass Skin Lesion classification approach using Transfer learning based convolutional Neural Network Abstract: The rapid rise in skin diseases over the past decade has been a growing concern worldwide. Early detection, correct categorization, and accurate identification can result in the successful treatment of melanoma, thereby

Author: Cauvery K, P C Siddalingaswamy, Sameena Pathan, Noel D'souza
Publish Year: 2021

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3 hours ago In this work, we have developed a Deep Convolutional Neural Network (CNN) to segment and classify the various types of breast abnormalities, such as calcifications, masses, asymmetry and carcinomas, unlike existing research work, which mainly classified the cancer into benign and malignant, leading to improved disease management.

Author: Maleika Heenaye-Mamode Khan, Nazmeen Boodoo-Jahangeer, Wasiimah Dullull, Shaista Nathire, Xiaohong G...
Publish Year: 2021
Author: Atif Mehmood, Muazzam Maqsood, Muzaffar Bashir, Yang Shuyuan
Publish Year: 2020

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Just Now To classify fruits more effectively, we propose a new deep convolutional neural network model to classify 118 fruits classes. The proposed model combines two aspects of …

Estimated Reading Time: 6 mins

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3 hours ago Convolutional Neural Networks (CNN) are one of the most remarkable approaches of deep learning, in which multiple layers of neurons are formed in a robust manner. They have shown that they are capable of demonstrating an impressive generalization capability on large data sets with millions of images [37], [38].

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7 hours ago Convolutional neural network (CNN) applied in this work. The recurrent neural network (RNN) has been successfully applied to process sequence data and had a wide range of applications in various fields including the processing of medical image . …

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4 hours ago Multi-Class Skin Diseases Classification Using Deep Convolutional Neural Network and Support Vector Machine Abstract: Globally, skin diseases are the fourth leading cause of non-fatal disease burden. Both high and low-income countries suffer from this burden; indicates the prevention of skin diseases should be prioritised.

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5 hours ago A Deep Convolutional Neural Network-Based Multi-Class Image Classification for Automatic Wafer Map Failure Recognition in Semiconductor Manufacturing by Huilin Zheng , Syed Waseem Abbas Sherazi , Sang Hyeok Son and Jong Yun Lee * Department of Computer Science, Chungbuk National University, Cheongju 28644, Korea *

Author: Huilin Zheng, Syed Waseem Abbas Sherazi, Sang Hyeok Son, Jong Yun Lee
Publish Year: 2021

Category: Cats Health Preview  Show details

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Frequently Asked Questions

Can we create a neural network for multi class classification from scratch?

In this article, we saw how we can create a very simple neural network for multi-class classification, from scratch in Python. This is the final article of the series: "Neural Network from Scratch in Python".

What is the feed forward step for a neural network with multi class output?

You can see that the feed-forward step for a neural network with multi-class output is pretty similar to the feed-forward step of the neural network for binary classification problems. The only difference is that here we are using softmax function at the output layer rather than the sigmoid function.

What is a convolutional neural network cnn?

As you can see, a Convolutional Neural Network can be interpreted as two sub networks where each sub network is responsible for performing a specific task. These Subnetworks are — Feature Learning Net (The Eyes of our CNN) and Classification Net (The Brain of our CNN).

Can deep convolutional neural network cnn help to classify breast abnormalities?

In this work, we have developed a Deep Convolutional Neural Network (CNN) to segment and classify the various types of breast abnormalities, such as calcifications, masses, asymmetry and carcinomas, unlike existing research work, which mainly classified the cancer into benign and malignant, leading to improved disease management.

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