scholarly journals Skin Lesion Segmentation in Dermoscopy Imagery

2022 ◽  
Author(s):  
Shelly Garg ◽  
Balkrishan Jindal
Keyword(s):  
Skin Lesion ◽  
Error Rate ◽  
Morphological Operations ◽  
Dice Coefficient ◽  
Skin Imaging ◽  
Average Accuracy ◽  
Proposed Model ◽  
Input Sample ◽  
Time Accuracy ◽  
Sensitivity Specificity

The main purpose of this study is to find an optimum method for segmentation of skin lesion images. In the present world, Skin cancer has proved to be the most deadly disease. The present research paper has developed a model which encompasses two gradations, the first being pre-processing for the reduction of unwanted artefacts like hair, illumination or many other by enhanced technique using threshold and morphological operations to attain higher accuracy and the second being segmentation by using k-mean with optimized Firefly Algorithm (FFA) technique. The online image database from the International Skin Imaging Collaboration (ISIC) archive dataset and dermatology service of Hospital Pedro Hispano (PH2) dataset has been used for input sample images. The parameters on which the proposed method is measured are sensitivity, specificity, dice coefficient, jacquard index, execution time, accuracy, error rate. From the results, authors have observed proposed model gives the average accuracy value of huge number of cancer images using ISIC dataset is 98.9% and using PH2 dataset is 99.1% with minimize average less error rate. It also estimates the dice coefficient value 0.993 using ISIC and 0.998 using PH2 datasets. However, the results for the rest of the parameters remain quite the same. Therefore the outcome of this model is highly reassuring.

HEp-2 CELL CLASSIFICATION BY ADAPTIVE CONVOLUTIONAL LAYER BASED CONVOLUTIONAL NEURAL NETWORK

2019 ◽  
Vol 31 (06) ◽  
pp. 1950044
Author(s):  
C. C. Manju ◽  
M. Victor Jose
Keyword(s):  
Neural Network ◽  
Autoimmune Diseases ◽  
Convolutional Neural Network ◽  
Research Work ◽  
Test Case ◽  
Morphological Operations ◽  
Cell Classification ◽  
Proposed Model ◽  
Sensitivity Specificity ◽  
Opening And Closing

Objective: The antinuclear antibodies (ANA) that present in the human serum have a link with various autoimmune diseases. Human Epithelial type-2 (HEp-2) cells acts as a substance in the Indirect Immuno fluorescence (IIF) test for diagnosing these autoimmune diseases. In recent times, the computer-aided diagnosis of autoimmune diseases by the HEp-2 cell classification has drawn more interest. Though, they often pose limitations like large intra-class and small inter-class variations. Hence, various efforts have been performed to automate the procedure of HEp-2 cell classification. To overcome these problems, this research work intends to propose a new HEp-2 classification process. Materials and Methods: This is regulated by integrating two processes, namely, segmentation and classification. Initially, the segmentation of the HEp-2 cells is carried out by deploying the morphological operations. In this paper, two morphology operations are deployed called opening and closing. Further, the classification process is exploited by proposing a modified Convolutional Neural Network (CNN). The main objective is to classify the HEp-2 cells effectively (Centromere, Golgi, Homogeneous, Nucleolar, NuMem, and Speckled) and is made by exploiting the optimization concept. This is implanted by developing a new algorithm called Distance Sorting Lion Algorithm (DSLA), which selects the optimal convolutional layer in CNN. Results: Through the performance analysis, the performance of the proposed model for test case 1 at learning percentage 60 is 3.84%, 1.79%, 6.22%, 1.69%, and 5.53% better than PSO, FF, GWO, WOA, and LA, respectively. At 80, the performance of the proposed model is 5.77%, 6.46%, 3.95%, 3.24%, and 5.55% better from PSO, FF, GWO, WOA, and LA, respectively. Hence, the performance of the proposed work is proved over other models under different measures. Conclusion: Finally, the performance is evaluated by comparing it with the other conventional algorithms in terms of accuracy, sensitivity, specificity, precision, FPR, FNR, NPV, MCC, F1-Score and FDR, and proves the efficacy of the proposed model.

scholarly journals Deep Hybrid Convolutional Neural Network for Segmentation of Melanoma Skin Lesion

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Cheng-Hong Yang ◽  
Jai-Hong Ren ◽  
Hsiu-Chen Huang ◽  
Li-Yeh Chuang ◽  
Po-Yin Chang
Keyword(s):  
Image Segmentation ◽  
Skin Cancer ◽  
Skin Lesion ◽  
Survival Rates ◽  
Skin Lesions ◽  
Manual Segmentation ◽  
Cure Rate ◽  
Dice Coefficient ◽  
Skin Imaging ◽  
Low Efficiency

Melanoma is a type of skin cancer that often leads to poor prognostic responses and survival rates. Melanoma usually develops in the limbs, including in fingers, palms, and the margins of the nails. When melanoma is detected early, surgical treatment may achieve a higher cure rate. The early diagnosis of melanoma depends on the manual segmentation of suspected lesions. However, manual segmentation can lead to problems, including misclassification and low efficiency. Therefore, it is essential to devise a method for automatic image segmentation that overcomes the aforementioned issues. In this study, an improved algorithm is proposed, termed EfficientUNet++, which is developed from the U-Net model. In EfficientUNet++, the pretrained EfficientNet model is added to the UNet++ model to accelerate segmentation process, leading to more reliable and precise results in skin cancer image segmentation. Two skin lesion datasets were used to compare the performance of the proposed EfficientUNet++ algorithm with other common models. In the PH2 dataset, EfficientUNet++ achieved a better Dice coefficient (93% vs. 76%–91%), Intersection over Union (IoU, 96% vs. 74%–95%), and loss value (30% vs. 44%–32%) compared with other models. In the International Skin Imaging Collaboration dataset, EfficientUNet++ obtained a similar Dice coefficient (96% vs. 94%–96%) but a better IoU (94% vs. 89%–93%) and loss value (11% vs. 13%–11%) than other models. In conclusion, the EfficientUNet++ model efficiently detects skin lesions by improving composite coefficients and structurally expanding the size of the convolution network. Moreover, the use of residual units deepens the network to further improve performance.

Concise CNN model for face expression recognition

2021 ◽  
pp. 1-9
Author(s):  
Harshadkumar B. Prajapati ◽  
Ankit S. Vyas ◽  
Vipul K. Dabhi
Keyword(s):  
Network Architecture ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Face Expression ◽  
Computational Overhead ◽  
Average Accuracy ◽  
Proposed Model ◽  
Face Expression Recognition ◽  
Expression Classification ◽  
Machine Interaction

Face expression recognition (FER) has gained very much attraction to researchers in the field of computer vision because of its major usefulness in security, robotics, and HMI (Human-Machine Interaction) systems. We propose a CNN (Convolutional Neural Network) architecture to address FER. To show the effectiveness of the proposed model, we evaluate the performance of the model on JAFFE dataset. We derive a concise CNN architecture to address the issue of expression classification. Objective of various experiments is to achieve convincing performance by reducing computational overhead. The proposed CNN model is very compact as compared to other state-of-the-art models. We could achieve highest accuracy of 97.10% and average accuracy of 90.43% for top 10 best runs without any pre-processing methods applied, which justifies the effectiveness of our model. Furthermore, we have also included visualization of CNN layers to observe the learning of CNN.

scholarly journals UNRAVELLING DIABETIC RETINOPATHY THROUGH IMAGE PROCESSING, NEURAL NETWORKS AND FUZZY LOGIC – A REVIEW

2017 ◽  
Vol 10 (4) ◽  
pp. 32
Author(s):  
Srinivasan A ◽  
Sudha S
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Diabetic Retinopathy ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Computation Time ◽  
Automated System ◽  
Morphological Operations ◽  
Advanced Stage ◽  
Sensitivity Specificity

One of the main causes of blindness is diabetic retinopathy (DR) and it may affect people of any ages. In these days, both young and old ages are affected by diabetes, and the di abetes is the main cause of DR. Hence, it is necessary to have an automated system with good accuracy and less computation time to diagnose and treat DR, and the automated system can simplify the work of ophthalmologists. The objective is to present an overview of various works recently in detecting and segmenting the various lesions of DR. Papers were categorized based on the diagnosing tools and the methods used for detecting early and advanced stage lesions. The early lesions of DR are microaneurysms, hemorrhages, exudates, and cotton wool spots and in the advanced stage, new and fragile blood vessels can be grown. Results have been evaluated in terms of sensitivity, specificity, accuracy and receiver operating characteristic curve. This paper analyzed the various steps and different algorithms used recently for the detection and classification of DR lesions. A comparison of performances has been made in terms of sensitivity, specificity, area under the curve, and accuracy. Suggestions, future workand the area to be improved were also discussed.Keywords: Diabetic retinopathy, Image processing, Morphological operations, Neural network, Fuzzy logic. 

scholarly journals Plant Disease Identification Using Shallow Convolutional Neural Network

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2388
Author(s):  
Sk Mahmudul Hassan ◽  
Michal Jasinski ◽  
Zbigniew Leonowicz ◽  
Elzbieta Jasinska ◽  
Arnab Kumar Maji
Keyword(s):  
Deep Learning ◽  
Late Blight ◽  
Early Blight ◽  
Gray Leaf Spot ◽  
Plant Diseases ◽  
Automated Identification ◽  
Average Accuracy ◽  
Proposed Model ◽  
Effective Manner ◽  
Tomato Diseases

Various plant diseases are major threats to agriculture. For timely control of different plant diseases in effective manner, automated identification of diseases are highly beneficial. So far, different techniques have been used to identify the diseases in plants. Deep learning is among the most widely used techniques in recent times due to its impressive results. In this work, we have proposed two methods namely shallow VGG with RF and shallow VGG with Xgboost to identify the diseases. The proposed model is compared with other hand-crafted and deep learning-based approaches. The experiments are carried on three different plants namely corn, potato, and tomato. The considered diseases in corns are Blight, Common rust, and Gray leaf spot, diseases in potatoes are early blight and late blight, and tomato diseases are bacterial spot, early blight, and late blight. The result shows that our implemented shallow VGG with Xgboost model outperforms different deep learning models in terms of accuracy, precision, recall, f1-score, and specificity. Shallow Visual Geometric Group (VGG) with Xgboost gives the highest accuracy rate of 94.47% in corn, 98.74% in potato, and 93.91% in the tomato dataset. The models are also tested with field images of potato, corn, and tomato. Even in field image the average accuracy obtained using shallow VGG with Xgboost are 94.22%, 97.36%, and 93.14%, respectively.

Attention-based Graph ResNet with focal loss for epileptic seizure detection

2021 ◽  
pp. 1-13
Author(s):  
Changxu Dong ◽  
Yanna Zhao ◽  
Gaobo Zhang ◽  
Mingrui Xue ◽  
Dengyu Chu ◽  
...  
Keyword(s):  
Class Imbalance ◽  
Seizure Detection ◽  
Brain Regions ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
Topological Relationships ◽  
Average Accuracy ◽  
Proposed Model ◽  
Eeg Data ◽  
The Central Nervous System

Epilepsy is a chronic brain disease resulted from the central nervous system lesion, which leads to repeated seizure occurs for the patients. Automatic seizure detection with Electroencephalogram (EEG) has witnessed great progress. However, existing methods paid little attention to the topological relationships of different EEG electrodes. Latest neuroscience researches have demonstrated the connectivity between different brain regions. Besides, class-imbalance is a common problem in EEG based seizure detection. The duration of epileptic EEG signals is much shorter than that of normal signals. In order to deal with the above mentioned two challenges, we propose to model the multi-channel EEG data using the Attention-based Graph ResNet (AGRN). In particular, each channel of the EEG signal represents a node of the graph and the inter-channel relations are modeled via the adjacency matrix in the graph. The loss function of the ARGN model is re-designed using focal loss to cope with the class-imbalance problem. The proposed ARGN with focal model could learn discriminative features from the raw EEG data. Experiments are carried out on the CHB-MIT dataset. The proposed model achieves an average accuracy of 98.70%, a sensitivity of 97.94%, a specificity of 98.66% and a precision of 98.62%. The Area Under the ROC Curve (AUC) is 98.69%.

scholarly journals Attention-Based Fully Gated CNN-BGRU for Russian Handwritten Text

2020 ◽  
Vol 6 (12) ◽  
pp. 141
Author(s):  
Abdelrahman Abdallah ◽  
Mohamed Hamada ◽  
Daniyar Nurseitov
Keyword(s):  
Error Rate ◽  
Handwriting Recognition ◽  
Text Recognition ◽  
P Value ◽  
Word Error Rate ◽  
Test Dataset ◽  
Handwritten Text ◽  
Proposed Model ◽  
Handwritten Text Recognition ◽  
Gated Recurrent Unit

This article considers the task of handwritten text recognition using attention-based encoder–decoder networks trained in the Kazakh and Russian languages. We have developed a novel deep neural network model based on a fully gated CNN, supported by multiple bidirectional gated recurrent unit (BGRU) and attention mechanisms to manipulate sophisticated features that achieve 0.045 Character Error Rate (CER), 0.192 Word Error Rate (WER), and 0.253 Sequence Error Rate (SER) for the first test dataset and 0.064 CER, 0.24 WER and 0.361 SER for the second test dataset. Our proposed model is the first work to handle handwriting recognition models in Kazakh and Russian languages. Our results confirm the importance of our proposed Attention-Gated-CNN-BGRU approach for training handwriting text recognition and indicate that it can lead to statistically significant improvements (p-value < 0.05) in the sensitivity (recall) over the tests dataset. The proposed method’s performance was evaluated using handwritten text databases of three languages: English, Russian, and Kazakh. It demonstrates better results on the Handwritten Kazakh and Russian (HKR) dataset than the other well-known models.

scholarly journals Deep Convolutional Neural Network Based ECG Classification System Using Information Fusion and One-Hot Encoding Techniques

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jia Li ◽  
Yujuan Si ◽  
Tao Xu ◽  
Saibiao Jiang
Keyword(s):  
Cardiovascular Disease ◽  
Adaptive Learning ◽  
Portable Devices ◽  
Ecg Signals ◽  
Automatic Feature Extraction ◽  
Average Accuracy ◽  
Proposed Model ◽  
Home Health Monitoring ◽  
Predictive Rate ◽  
Electrocardiogram Ecg

Although convolutional neural networks (CNNs) can be used to classify electrocardiogram (ECG) beats in the diagnosis of cardiovascular disease, ECG signals are typically processed as one-dimensional signals while CNNs are better suited to multidimensional pattern or image recognition applications. In this study, the morphology and rhythm of heartbeats are fused into a two-dimensional information vector for subsequent processing by CNNs that include adaptive learning rate and biased dropout methods. The results demonstrate that the proposed CNN model is effective for detecting irregular heartbeats or arrhythmias via automatic feature extraction. When the proposed model was tested on the MIT-BIH arrhythmia database, the model achieved higher performance than other state-of-the-art methods for five and eight heartbeat categories (the average accuracy was 99.1% and 97%). In particular, the proposed system had better performance in terms of the sensitivity and positive predictive rate for V beats by more than 4.3% and 5.4%, respectively, and also for S beats by more than 22.6% and 25.9%, respectively, when compared to existing algorithms. It is anticipated that the proposed method will be suitable for implementation on portable devices for the e-home health monitoring of cardiovascular disease.

scholarly journals Performance Analysis of Grouped Multilevel Space-Time Trellis Coding Technique Using Cognitive Radio in Different Deployment Models

2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Shakti Raj Chopra ◽  
Akhil Gupta ◽  
Rakesh Kumar Jha
Keyword(s):  
Cognitive Radio ◽  
Error Rate ◽  
Cooperative Diversity ◽  
Symbol Error Rate ◽  
Space Time ◽  
Link Quality ◽  
Trellis Coding ◽  
Space Time Coding ◽  
Proposed Model

In this era, the number of users in a network is increasing tremendously at a faster rate; as a consequence, quality of service (QoS) is drastically deteriorating. To compensate such kinds of problems, we attempted to enhance the QoS of the network, which leads to an improvement in throughput, link quality, spectral efficiency, and many more. To meet the requirements mentioned above, many researchers intervene to advance and propose different techniques with an appropriate design methodology. In this work, we try to emphasize symbol error rate (SER) and frame error rate (FER) by implementing some of the existing space-time coding techniques like Space-Time Trellis Coding (STTC), multilevel space-time trellis coding (MLSTTC), and grouped multilevel space-time trellis coding (GMLSTTC). Though all these techniques are proved to be efficient enough, we explicitly included a powerful method of cooperative diversity-based spectrum sensing in cognitive radio scenario. From this analysis, we landed on to the conclusion that this technique is far better to deal with all these parameters, which can improve the QoS of the network. This paper has also analyzed the effect of the proposed model of GMLSTTC with cognitive radio on various deployment setups such as urban, suburban, and rural macrodeployment setup of the ITU-R M.2135 standard.

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