Comparative Analysis and Proposal of Deep Learning Based Colorectal Cancer Polyps Classification Technique

2020 ◽  
Vol 17 (5) ◽  
pp. 2354-2362
Author(s):  
Sushama Tanwar ◽  
S. Vijayalakshmi

The information hidden in an image is worth more than a thousand words. Proper analysis of a medical image can help in timely detection and diagnose of a disease which increases the rate of survival of cancer patients. Analysis of images manually is subjective and time consuming. On the other hand, automated analysis of a medical image has a lot of challenges due to the architecture and colors of the medical images. This paper, gives a survey on detection, classification and diagnosis of colorectal cancer and proposes a deep learning based techniques to differentiate between healthy tissues and cancerous polyps in histology images. It also compares the accuracy of three different classification frameworks namely Convolutional Neural Network (CNN), Fully Convolutional Network (FCN) and Recurrent Neural Network (RNN). It also presents the overview of the work done in this field. It first discusses basic deep learning methods and then the known techniques used for detection, classification and diagnosis of colorectal cancer followed by the comparative analysis of all the surveyed paper. Finally, it talks about the conclusion, challenges and the future scope of the progress in this field.

2020 ◽  
Author(s):  
Kangling Lin ◽  
Hua Chen ◽  
Chong-Yu Xu ◽  
Yanlai Zhou ◽  
Shenglian Guo

<p>With the rapid growth of deep learning recently, artificial neural networks have been propelled to the forefront in flood forecasting via their end-to-end learning ability. Encoder-decoder architecture, as a novel deep feature extraction, which captures the inherent relationship of the data involved, has emerged in time sequence forecasting nowadays. As the advance of encoder-decoder architecture in sequence to sequence learning, it has been applied in many fields, such as machine translation, energy and environment. However, it is seldom used in hydrological modelling. In this study, a new neural network is developed to forecast flood based on the encoder-decoder architecture. There are two deep learning methods, including the Long Short-Term Memory (LSTM) network and Temporal Convolutional Network (TCN), selected as encoders respectively, while the LSTM was also chosen as the decoder, whose results are compared with those from the standard LSTM without using encoder-decoder architecture.</p><p>These models were trained and tested by using the hourly flood events data from 2009 to 2015 in Jianxi basin, China. The results indicated that the new neural flood forecasting networks based encoder-decoder architectures generally perform better than the standard LSTM, since they have better goodness-of-fit between forecasted and observed flood and produce the promising performance in multi-index assessment. The TCN as an encoder has better model stability and accuracy than LSTM as an encoder, especially in longer forecast periods and larger flood. The study results also show that the encoder-decoder architecture can be used as an effective deep learning solution in flood forecasting.</p><p></p>


2019 ◽  
Vol 8 (3) ◽  
pp. 6873-6880

Palm leaf manuscripts has been one of the ancient writing methods but the palm leaf manuscripts content requires to be inscribed in a new set of leaves. This study has provided a solution to save the contents in palm leaf manuscripts by recognizing the handwritten Tamil characters in manuscripts and storing them digitally. Character recognition is one of the most essential fields of pattern recognition and image processing. Generally Optical character recognition is the method of e-translation of typewritten text or handwritten images into machine editable text. The handwritten Tamil character recognition has been one of the challenging and active areas of research in the field of pattern recognition and image processing. In this study a trial was made to identify Tamil handwritten characters without extraction of feature using convolutional neural networks. This study uses convolutional neural networks for recognizing and classifying the Tamil palm leaf manuscripts of characters from separated character images. The convolutional neural network is a deep learning approach for which it does not need to retrieve features and also a rapid approach for character recognition. In the proposed system every character is expanded to needed pixels. The expanded characters have predetermined pixels and these pixels are considered as characteristics for neural network training. The trained network is employed for recognition and classification. Convolutional Network Model development contains convolution layer, Relu layer, pooling layer, fully connected layer. The ancient Tamil character dataset of 60 varying class has been created. The outputs reveal that the proposed approach generates better rates of recognition than that of schemes based on feature extraction for handwritten character recognition. The accuracy of the proposed approach has been identified as 97% which shows that the proposed approach is effective in terms of recognition of ancient characters.


2021 ◽  
Vol 6 (2) ◽  
pp. 236-251
Author(s):  
Nadia Azahro Choirunisa ◽  
Tita Karlita ◽  
Rengga Asmara

Kucing merupakan hewan yang sangat popular di dunia. Jumlah dari ras kucing di dunia hanya sekitar 1% saja, sehingga didominasi oleh ras campuran maupun kucing domestik. Namun demikian, ada begitu banyak jenis ras kucing di dunia, sehingga terkadang sulit untuk mengidentifikasinya. Oleh karena itu, dibutuhkan sistem yang dapat mengenali jenis-jenis ras kucing. Dalam penelitian ini, penulis menggunakan salah satu metode deep learning yang dapat mengenali dan mengklasifikasikan suatu objek, yaitu Neural Convolutional Network (CNN). Penulis menggunakan 9 jenis ras kucing yang berbeda berisi 2700 gambar. Dalam pengujiannya, penulis menggunakan arsitektur EfficientNet-B0. Model paling optimal dari pengujian yang dilakukan terhadap 180 gambar kucing memperoleh tingkat akurasi sebesar 95%.   Kata Kunci : Deep Learning, Convolutional Neural Network (CNN) , Ras kucing, EfficientNet-B0.


2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Takuya Maekawa ◽  
Kazuya Ohara ◽  
Yizhe Zhang ◽  
Matasaburo Fukutomi ◽  
Sakiko Matsumoto ◽  
...  

Abstract A comparative analysis of animal behavior (e.g., male vs. female groups) has been widely used to elucidate behavior specific to one group since pre-Darwinian times. However, big data generated by new sensing technologies, e.g., GPS, makes it difficult for them to contrast group differences manually. This study introduces DeepHL, a deep learning-assisted platform for the comparative analysis of animal movement data, i.e., trajectories. This software uses a deep neural network based on an attention mechanism to automatically detect segments in trajectories that are characteristic of one group. It then highlights these segments in visualized trajectories, enabling biologists to focus on these segments, and helps them reveal the underlying meaning of the highlighted segments to facilitate formulating new hypotheses. We tested the platform on a variety of trajectories of worms, insects, mice, bears, and seabirds across a scale from millimeters to hundreds of kilometers, revealing new movement features of these animals.


2019 ◽  
Vol 11 (24) ◽  
pp. 2997 ◽  
Author(s):  
Clément Dechesne ◽  
Sébastien Lefèvre ◽  
Rodolphe Vadaine ◽  
Guillaume Hajduch ◽  
Ronan Fablet

The monitoring and surveillance of maritime activities are critical issues in both military and civilian fields, including among others fisheries’ monitoring, maritime traffic surveillance, coastal and at-sea safety operations, and tactical situations. In operational contexts, ship detection and identification is traditionally performed by a human observer who identifies all kinds of ships from a visual analysis of remotely sensed images. Such a task is very time consuming and cannot be conducted at a very large scale, while Sentinel-1 SAR data now provide a regular and worldwide coverage. Meanwhile, with the emergence of GPUs, deep learning methods are now established as state-of-the-art solutions for computer vision, replacing human intervention in many contexts. They have been shown to be adapted for ship detection, most often with very high resolution SAR or optical imagery. In this paper, we go one step further and investigate a deep neural network for the joint classification and characterization of ships from SAR Sentinel-1 data. We benefit from the synergies between AIS (Automatic Identification System) and Sentinel-1 data to build significant training datasets. We design a multi-task neural network architecture composed of one joint convolutional network connected to three task specific networks, namely for ship detection, classification, and length estimation. The experimental assessment shows that our network provides promising results, with accurate classification and length performance (classification overall accuracy: 97.25%, mean length error: 4.65 m ± 8.55 m).


Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 245
Author(s):  
Ornela Bardhi ◽  
Daniel Sierra-Sosa ◽  
Begonya Garcia-Zapirain ◽  
Luis Bujanda

Colorectal cancer is one of the main causes of cancer incident cases and cancer deaths worldwide. Undetected colon polyps, be them benign or malignant, lead to late diagnosis of colorectal cancer. Computer aided devices have helped to decrease the polyp miss rate. The application of deep learning algorithms and techniques has escalated during this last decade. Many scientific studies are published to detect, localize, and classify colon polyps. We present here a brief review of the latest published studies. We compare the accuracy of these studies with our results obtained from training and testing three independent datasets using a convolutional neural network and autoencoder model. A train, validate and test split was performed for each dataset, 75%, 15%, and 15%, respectively. An accuracy of 0.937 was achieved for CVC-ColonDB, 0.951 for CVC-ClinicDB, and 0.967 for ETIS-LaribPolypDB. Our results suggest slight improvements compared to the algorithms used to date.


2021 ◽  
Vol 2137 (1) ◽  
pp. 012056
Author(s):  
Hongli Ma ◽  
Fang Xie ◽  
Tao Chen ◽  
Lei Liang ◽  
Jie Lu

Abstract Convolutional neural network is a very important research direction in deep learning technology. According to the current development of convolutional network, in this paper, convolutional neural networks are induced. Firstly, this paper induces the development process of convolutional neural network; then it introduces the structure of convolutional neural network and some typical convolutional neural networks. Finally, several examples of the application of deep learning is introduced.


Author(s):  
Quazi Ghulam Rafi ◽  
◽  
Mohammed Noman ◽  
Sadia Zahin Prodhan ◽  
Sabrina Alam ◽  
...  

Among the many music information retrieval (MIR) tasks, music genre classification is noteworthy. The categorization of music into different groups that came to existence through a complex interplay of cultures, musicians, and various market forces to characterize similarities between compositions and organize collections is known as a music genre. The past researchers extracted various hand-crafted features and developed classifiers based on them. But the major drawback of this approach was the requirement of field expertise. However, in recent times researchers, because of the remarkable classification accuracy of deep learning models, have used similar models for MIR tasks. Convolutional Neural Net- work (CNN), Recurrent Neural Network (RNN), and the hybrid model, Convolutional - Recurrent Neural Network (CRNN), are such prominently used deep learning models for music genre classification along with other MIR tasks and various architectures of these models have achieved state-of-the-art results. In this study, we review and discuss three such architectures of deep learning models, already used for music genre classification of music tracks of length of 29-30 seconds. In particular, we analyze improved CNN, RNN, and CRNN architectures named Bottom-up Broadcast Neural Network (BBNN) [1], Independent Recurrent Neural Network (IndRNN) [2] and CRNN in Time and Frequency dimensions (CRNN- TF) [3] respectively, almost all of the architectures achieved the highest classification accuracy among the variants of their base deep learning model. Hence, this study holds a comparative analysis of the three most impressive architectural variants of the main deep learning models that are prominently used to classify music genre and presents the three architecture, hence the models (CNN, RNN, and CRNN) in one study. We also propose two ways that can improve the performances of the RNN (IndRNN) and CRNN (CRNN-TF) architectures.


2018 ◽  
Vol 11 (2) ◽  
pp. 59 ◽  
Author(s):  
Yohanes Gultom ◽  
Aniati Murni Arymurthy ◽  
Rian Josua Masikome

Batik fabric is one of the most profound cultural heritage in Indonesia. Hence, continuous research on understanding it is necessary to preserve it. Despite of being one of the most common research task, Batik’s pattern automatic classification still requires some improvement especially in regards to invariance dilemma. Convolutional neural network (ConvNet) is one of deep learning architecture which able to learn data representation by combining local receptive inputs, weight sharing and convolutions in order to solve invariance dilemma in image classification. Using dataset of 2,092 Batik patches (5 classes), the experiments show that the proposed model, which used deep ConvNet VGG16 as feature extractor (transfer learning), achieves slightly better average of 89 ± 7% accuracy than SIFT and SURF-based that achieve 88 ± 10% and 88 ± 8% respectively. Despite of that, SIFT reaches around 5% better accuracy in rotated and scaled dataset.


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