Detection and Diagnosis Gray Spots on Tea Leaves Using Computer Vision and Multi-layer Perceptron

2019 ◽  
pp. 229-237
Author(s):  
Pham Thanh Binh ◽  
Tang Cam Nhung ◽  
Dao Huy Du
Keyword(s):  
Computer Vision ◽  
Multi Layer Perceptron ◽  
Tea Leaves ◽  
Detection And Diagnosis

A cost-effective computer-vision based breast cancer diagnosis

2021 ◽  
pp. 1-11
Author(s):  
Prabira Kumar Sethy ◽  
Chanki Pandey ◽  
Dr. Mohammad Rafique Khan ◽  
Santi Kumari Behera ◽  
K. Vijaykumar ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Computer Vision ◽  
Cancer Detection ◽  
Cost Effective ◽  
Breast Cancer Diagnosis ◽  
Breast Conserving Surgery ◽  
Breast Cancer Detection ◽  
World Health ◽  
Partial Mastectomy ◽  
Detection And Diagnosis

In the last decade, there have been extensive reports of world health organization (WHO) on breast cancer. About 2.1 million women are affected every year and it is the second most leading cause of cancer death in women. Initial detection and diagnosis of cancer appreciably increase the chance of saving lives and reduce treatment costs. In this paper, we perform a survey of the techniques utilized in breast cancer detection and diagnosis in image processing, machine learning (ML), and deep learning (DL). We also proposed a novel computer-vision based cost-effective method for breast cancer detection and diagnosis. Along with the detection and diagnosis of breast cancer, our proposed method is capable of finding the exact position of the abnormality present in the breast that will help in breast-conserving surgery or partial mastectomy. The proposed method is the simplest and cost-effective approach that has produced highly accurate and useful outcomes when compared with the existing approach.

scholarly journals COMPUTER VISION FOR SKIN CANCER DETECTION AND DIAGNOSIS

2020 ◽  
Author(s):  
Farah Shahata ◽  
Kamalpreet Kaur ◽  
Jinan Fiaidhi
Keyword(s):  
Computer Vision ◽  
Skin Cancer ◽  
Web Service ◽  
Cancer Detection ◽  
Skin Imaging ◽  
Proposed Model ◽  
Diagnosis Accuracy ◽  
Detection And Diagnosis ◽  
Skin Cancer Detection ◽  
Multi Classification

<b>— Skin-cancer is the most common type of cancer among all other types of cancers spreading both developed and developing countries. In this paper, a web service is developed in order to help physicians as well as individuals to upload an image and diagnose the particular types of lesion. Computer vision is used to analyse lesions on images by providing computers with somewhat similarity as humans do. For this a Convolution Neural Network (CNN) is used with multi classification on International Skin Imaging Collaboration (ISIC) 2018 dataset with HAM10000 images. This dataset is a meta-data which has various types of images with seven different labels. At first, the model is trained with a larger training set and saved in a zip folder. Secondly, a web service is created where users or a doctor can upload an image for classification. Thirdly, the images uploaded are pre-processed as there is noise, hairs on image. Techniques like resizing, normalisation, thresholding, black-hat filtering and inpainting are used for this purpose. After this, the saved model is called to define whether the uploaded image is benign or malignant. The experimental results reveal that the proposed model is superior in terms of detection and diagnosis accuracy as compared to modern methods.</b>

scholarly journals Grape Drying Process Using Machine Vision Based on Multilayer Perceptron Networks

2020 ◽  
Vol 5 (3) ◽  
pp. 382-394
Author(s):  
Suprapto Suprapto ◽  
Edy Riyanto
Keyword(s):  
Computer Vision ◽  
Machine Vision ◽  
Motion Control ◽  
Multilayer Perceptron ◽  
Multi Layer Perceptron ◽  
Drying Process ◽  
Different Temperatures ◽  
Motion Speed ◽  
Similar Area ◽  
Multilayer Perceptron Networks

This paper proposed a grape drying machine using computer vision and Multi-layer Perceptron (MLP) method. Computer vision is for taking grapes’ image on conveyor, whereas MLP is for controlling grape drying machine and classifying its output. To evaluate the proposed, a kind of grapes are put on conveyor of the machine and their images are taken every two min. Some parameters of MLP to control the drying machine includes dried grape, temperature, grape area, motor position, and motion speed. Those parameters are to adjust an appropriate MLP’s output, including motion control and heater control. Two different temperatures are employed on the machine, including 60 and 75°C. The results showed that the grape could be dried with similar area 3800 pixel at the 770th min using temperature 60°C and at the 410th min using temperature 75°C.  Comparing between them, the similar ratio could also be achieved at 0.64 with different time 360 min. Indeed, the temperature setting at 75°C resulted faster drying performance.

scholarly journals Slantlet transform used for faults diagnosis in robot arm

2022 ◽  
Vol 25 (1) ◽  
pp. 281
Author(s):  
Muhamad Azhar Abdilatef Alobaidy ◽  
Jassim Mohammed Abdul-Jabbar ◽  
Saad Zaghlul Al-khayyt
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fault Detection And Diagnosis ◽  
Discrete Wavelet ◽  
Robot Arm ◽  
Multi Layer Perceptron ◽  
Artificial Neural ◽  
Detection And Diagnosis

<p class="JESTECAbstract">The <span>robot arm systems are the most target systems in the fields of faults detection and diagnosis which are electrical and the mechanical systems in many fields. Fault detection and diagnosis study is presented for two robot arms. The disturbance due to the faults at robot's joints causes oscillations at the tip of the robot arm. The acceleration in multi-direction is analysed to extract the features of the faults. Simulations for planar and space robots are presented. Two types of feature (faults) detection methods are used in this paper. The first one is the discrete wavelet transform, which is applied in many research's works before. The second type, is the Slantlet transform, which represents an improved model of the discrete wavelet transform. The multi-layer perceptron artificial neural network is used for the purpose of faults allocation and classification. According to the obtained results, the Slantlet transform with the multi-layer perceptron artificial neural network appear to possess best performance (4.7088e-05), lower consuming time <br /> (71.017308 sec) and higher accuracy (100%) than the results obtained when applying discrete wavelet transform and artificial neural network for the same </span>purpose.</p>

scholarly journals Rancang Bangun Klasifikasi Citra Dengan Teknologi Deep Learning Berbasis Metode Convolutional Neural Network

2020 ◽  
Vol 8 (2) ◽  
pp. 138
Author(s):  
Ari Peryanto ◽  
Anton Yudhana ◽  
Rusydi Umar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Gpu Acceleration ◽  
Multi Layer Perceptron

Dengan berkembang pesatnya teknologi saat ini, mengakibatkan Deep Learning menjadi salah satu metode machine learning yang sangat diminati. Teknologi GPU Acceleration menjadi salah satu sebab dari pesatnya perkembangan Deep Learning. Deep learning sangat cocok digunakan untuk memecahkan permasalahan klasik dalam Computer Vision, yaitu dalam pengklasifikasian citra. Salah satu metode dalam deep  learning yang  sering digunakan dalam pengolah  citra  adalah  Convolutional Neural Network dan merupakan pengembangan dari Multi Layer Perceptron. Pada penelitian ini pengimplementasian  metode ini dilakukan  menggunakan library  keras dengan bahasa pemrograman phyton.  Pada  proses  training  menggunakan  Convolutional  Neural  Network,  dilakukan  setting  jumlah epoch dan memperbesar ukuran data training untuk meningkatkan akurasi dalam pengklasifikasian citra. Ukuran yang digunakan adalah 32x32, 64x64 dan 128x128. Proses training dengan jumlah epoch 40 dan ukuran 32x32 didapat nilai akurasi tertinggi yang mencapai 98,02% dan rata-rata akurasi tertinggi yaitu 97,56 %, serta  akurasi sistem sebesar 96,64%.

Deep Learning Models for Detection and Diagnosis of Alzheimer's Disease

2021 ◽  
pp. 140-149
Author(s):  
Gowhar Mohiuddin Dar ◽  
Ashok Sharma ◽  
Parveen Singh
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Reinforcement Learning ◽  
Natural Language ◽  
Language Processing ◽  
Computational Techniques ◽  
Medical Sciences ◽  
Data Application ◽  
Detection And Diagnosis

The chapter explores the implications of deep learning in medical sciences, focusing on deep learning concerning natural language processing, computer vision, reinforcement learning, big data, and blockchain influence on some areas of medicine and construction of end-to-end systems with the help of these computational techniques. The deliberation of computer vision in the study is mainly concerned with medical imaging and further usage of natural language processing to spheres such as electronic wellbeing record data. Application of deep learning in genetic mapping and DNA sequencing termed as genomics and implications of reinforcement learning about surgeries assisted by robots are also overviewed.

scholarly journals COMPUTER VISION FOR SKIN CANCER DETECTION AND DIAGNOSIS

2020 ◽  
Author(s):  
Farah Shahata ◽  
Kamalpreet Kaur ◽  
Jinan Fiaidhi
Keyword(s):  
Computer Vision ◽  
Skin Cancer ◽  
Web Service ◽  
Cancer Detection ◽  
Skin Imaging ◽  
Proposed Model ◽  
Diagnosis Accuracy ◽  
Detection And Diagnosis ◽  
Skin Cancer Detection ◽  
Multi Classification

<b>— Skin-cancer is the most common type of cancer among all other types of cancers spreading both developed and developing countries. In this paper, a web service is developed in order to help physicians as well as individuals to upload an image and diagnose the particular types of lesion. Computer vision is used to analyse lesions on images by providing computers with somewhat similarity as humans do. For this a Convolution Neural Network (CNN) is used with multi classification on International Skin Imaging Collaboration (ISIC) 2018 dataset with HAM10000 images. This dataset is a meta-data which has various types of images with seven different labels. At first, the model is trained with a larger training set and saved in a zip folder. Secondly, a web service is created where users or a doctor can upload an image for classification. Thirdly, the images uploaded are pre-processed as there is noise, hairs on image. Techniques like resizing, normalisation, thresholding, black-hat filtering and inpainting are used for this purpose. After this, the saved model is called to define whether the uploaded image is benign or malignant. The experimental results reveal that the proposed model is superior in terms of detection and diagnosis accuracy as compared to modern methods.</b>

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