scholarly journals Convolutional Neural Networks for Classifying Melanoma Images

2020 ◽  
Author(s):  
Abhinav Sagar ◽  
J Dheeba
Keyword(s):  
Neural Networks ◽  
Skin Cancer ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Cancer Classification ◽  
Previous State ◽  
Better Than

AbstractIn this work, we address the problem of skin cancer classification using convolutional neural networks. A lot of cancer cases early on are misdiagnosed as something else leading to severe consequences including the death of a patient. Also there are cases in which patients have some other problems and doctors think they might have skin cancer. This leads to unnecessary time and money spent for further diagnosis. In this work, we address both of the above problems using deep neural networks and transfer learning architecture. We have used publicly available ISIC databases for both training and testing our model. Our work achieves an accuracy of 0.935, precision of 0.94, recall of 0.77, F1 score of 0.85 and ROC-AUC of 0.861 which is better than the previous state of the art approaches.

scholarly journals Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy

2021 ◽  
Author(s):  
Jorge F. Lazo ◽  
Aldo Marzullo ◽  
Sara Moccia ◽  
Michele Catellani ◽  
Benoit Rosa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
State Of The Art ◽  
Automatic Segmentation ◽  
Temporal Information ◽  
Invasive Technique ◽  
Dice Similarity Coefficient ◽  
Specular Reflections ◽  
Lumen Segmentation ◽  
Previous State

Abstract Purpose Ureteroscopy is an efficient endoscopic minimally invasive technique for the diagnosis and treatment of upper tract urothelial carcinoma. During ureteroscopy, the automatic segmentation of the hollow lumen is of primary importance, since it indicates the path that the endoscope should follow. In order to obtain an accurate segmentation of the hollow lumen, this paper presents an automatic method based on convolutional neural networks (CNNs). Methods The proposed method is based on an ensemble of 4 parallel CNNs to simultaneously process single and multi-frame information. Of these, two architectures are taken as core-models, namely U-Net based in residual blocks ($$m_1$$ m 1 ) and Mask-RCNN ($$m_2$$ m 2 ), which are fed with single still-frames I(t). The other two models ($$M_1$$ M 1 , $$M_2$$ M 2 ) are modifications of the former ones consisting on the addition of a stage which makes use of 3D convolutions to process temporal information. $$M_1$$ M 1 , $$M_2$$ M 2 are fed with triplets of frames ($$I(t-1)$$ I ( t - 1 ) , I(t), $$I(t+1)$$ I ( t + 1 ) ) to produce the segmentation for I(t). Results The proposed method was evaluated using a custom dataset of 11 videos (2673 frames) which were collected and manually annotated from 6 patients. We obtain a Dice similarity coefficient of 0.80, outperforming previous state-of-the-art methods. Conclusion The obtained results show that spatial-temporal information can be effectively exploited by the ensemble model to improve hollow lumen segmentation in ureteroscopic images. The method is effective also in the presence of poor visibility, occasional bleeding, or specular reflections.

scholarly journals Hybridizing Evolutionary Computation and Deep Neural Networks: An Approach to Handwriting Recognition Using Committees and Transfer Learning

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Alejandro Baldominos ◽  
Yago Saez ◽  
Pedro Isasi
Keyword(s):  
Neural Networks ◽  
Evolutionary Computation ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Character Recognition ◽  
Deep Neural Networks ◽  
Handwriting Recognition ◽  
Computational Time ◽  
Learning Models ◽  
Expensive Process

Neuroevolution is the field of study that uses evolutionary computation in order to optimize certain aspect of the design of neural networks, most often its topology and hyperparameters. The field was introduced in the late-1980s, but only in the latest years the field has become mature enough to enable the optimization of deep learning models, such as convolutional neural networks. In this paper, we rely on previous work to apply neuroevolution in order to optimize the topology of deep neural networks that can be used to solve the problem of handwritten character recognition. Moreover, we take advantage of the fact that evolutionary algorithms optimize a population of candidate solutions, by combining a set of the best evolved models resulting in a committee of convolutional neural networks. This process is enhanced by using specific mechanisms to preserve the diversity of the population. Additionally, in this paper, we address one of the disadvantages of neuroevolution: the process is very expensive in terms of computational time. To lessen this issue, we explore the performance of topology transfer learning: whether the best topology obtained using neuroevolution for a certain domain can be successfully applied to a different domain. By doing so, the expensive process of neuroevolution can be reused to tackle different problems, turning it into a more appealing approach for optimizing the design of neural networks topologies. After evaluating our proposal, results show that both the use of neuroevolved committees and the application of topology transfer learning are successful: committees of convolutional neural networks are able to improve classification results when compared to single models, and topologies learned for one problem can be reused for a different problem and data with a good performance. Additionally, both approaches can be combined by building committees of transferred topologies, and this combination attains results that combine the best of both approaches.

scholarly journals Sentiment Classification Using Convolutional Neural Networks

2019 ◽  
Vol 9 (11) ◽  
pp. 2347 ◽  
Author(s):  
Hannah Kim ◽  
Young-Seob Jeong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Text Classification ◽  
State Of The Art ◽  
Sentiment Classification ◽  
Learning Models ◽  
Text Data ◽  
Textual Data ◽  
Better Than

As the number of textual data is exponentially increasing, it becomes more important to develop models to analyze the text data automatically. The texts may contain various labels such as gender, age, country, sentiment, and so forth. Using such labels may bring benefits to some industrial fields, so many studies of text classification have appeared. Recently, the Convolutional Neural Network (CNN) has been adopted for the task of text classification and has shown quite successful results. In this paper, we propose convolutional neural networks for the task of sentiment classification. Through experiments with three well-known datasets, we show that employing consecutive convolutional layers is effective for relatively longer texts, and our networks are better than other state-of-the-art deep learning models.

Outperforming Dermatologist-Level Skin Cancer Classification via Enhanced Training of Deep Neural Networks (Preprint)

2018 ◽  
Author(s):  
Titus Josef Brinker ◽  
Achim Hekler ◽  
Christof von Kalle
Keyword(s):  
Neural Networks ◽  
Skin Cancer ◽  
Deep Neural Networks ◽  
Challenge Test ◽  
Image Data ◽  
Cancer Classification ◽  
Receiver Operating Curve ◽  
Training Procedure ◽  
Average Precision ◽  
Test Sets

BACKGROUND In recent months, multiple publications have demonstrated the use of convolutional neural networks (CNN) to classify images of skin cancer as precisely as dermatologists. These CNNs failed to outperform the International Symposium on Biomedical Imaging (ISBI) 2016 challenge in terms of average precision, however, so the technical progress represented by these studies is limited. In addition, the available reports are difficult to reproduce, due to incomplete descriptions of training procedures and the use of proprietary image databases. These factors prevent the comparison of various CNN classifiers in equal terms. OBJECTIVE To demonstrate the training of an image-classifier CNN that outperforms the winner of the ISBI 2016 challenge by using open source images exclusively. METHODS A detailed description of the training procedure is reported while the used images and test sets are disclosed fully, to insure the reproducibility of our work. RESULTS Our CNN classifier outperforms all recent attempts to classify the original ISBI 2016 challenge test data (full set of 379 test images), with an average precision of 0.709 (vs. 0.637 of the ISBI winner) and with an area under the receiver operating curve of 0.85. CONCLUSIONS This work illustrates the potential for improving skin cancer classification with enhanced training procedures for CNNs, while avoiding the use of costly equipment or proprietary image data.

Classification of Skin Cancer Lesions Using Deep Neural Networks and Transfer Learning

2021 ◽  
pp. 259-268
Author(s):  
Danny Joel Devarapalli ◽  
Venkata Sai Dheeraj Mavilla ◽  
Sai Prashanth Reddy Karri ◽  
Harshit Gorijavolu ◽  
Sri Anjaneya Nimmakuri
Keyword(s):  
Neural Networks ◽  
Skin Cancer ◽  
Transfer Learning ◽  
Deep Neural Networks

Skin cancer classification using Convolutional neural networks

2021 ◽  
Author(s):  
R Raja Subramanian ◽  
Dintakurthi Achuth ◽  
P Shiridi Kumar ◽  
Kovvuru Naveen kumar Reddy ◽  
Srikar Amara ◽  
...  
Keyword(s):  
Neural Networks ◽  
Skin Cancer ◽  
Convolutional Neural Networks ◽  
Cancer Classification

scholarly journals Skin Cancer Classification Using Convolutional Neural Networks with Integrated Patient Data: A Systematic Review (Preprint)

10.2196/20708 ◽  
2020 ◽  
Author(s):  
Julia Höhn ◽  
Achim Hekler ◽  
Eva Krieghoff-Henning ◽  
Jakob Nikolas Kather ◽  
Jochen Sven Utikal ◽  
...  
Keyword(s):  
Systematic Review ◽  
Neural Networks ◽  
Skin Cancer ◽  
Convolutional Neural Networks ◽  
Patient Data ◽  
Cancer Classification

scholarly journals Towards Robust Object detection in Floor Plan Images: A Data Augmentation Approach

2021 ◽  
Author(s):  
Shashank Mishra ◽  
Khurram Azeem Hashmi ◽  
Alain Pagani ◽  
Marcus Liwicki ◽  
Didier Stricker ◽  
...  
Keyword(s):  
Object Detection ◽  
Deep Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
The Novel ◽  
Floor Plan ◽  
Previous State ◽  
Floor Plans ◽  
Public Datasets ◽  
Better Than

Object detection is one of the most critical tasks in the field of Computer vision. This task comprises identifying and localizing an object in the image. Architectural floor plans represent the layout of buildings and apartments. The floor plans consist of walls, windows, stairs, and other furniture objects. While recognizing floor plan objects is straightforward for humans, automatically processing floor plans and recognizing objects is a challenging problem. In this work, we investigate the performance of the recently introduced Cascade Mask R-CNN network to solve object detection in floor plan images. Furthermore, we experimentally establish that deformable convolution works better than conventional convolutions in the proposed framework. Identifying objects in floor plan images is also challenging due to the variety of floor plans and different objects. We faced a problem in training our network because of the lack of publicly available datasets. Currently, available public datasets do not have enough images to train deep neural networks efficiently. We introduce SFPI, a novel synthetic floor plan dataset consisting of 10000 images to address this issue. Our proposed method conveniently surpasses the previous state-of-the-art results on the SESYD dataset and sets impressive baseline results on the proposed SFPI dataset. The dataset can be downloaded from SFPI Dataset Link. We believe that the novel dataset enables the researcher to enhance the research in this domain further.

Sign in / Sign up

Export Citation Format

Share Document