Skin Cancer Diseases Classification using Deep Convolutional Neural Network with Transfer Learning Model

Abstract Skin cancer is becoming increasingly common. Fortunately, early discovery can greatly improve the odds of a patient being healed. Many Artificial Intelligence based approaches to classify skin lesions have recently been proposed. but these approaches suffer from limited classification accuracy. Deep convolutional neural networks show potential for better classification of cancer lesions. This paper presents a fine-tuning on Xception pretrained model for classification of skin lesions by adding a group of layers after the basic ones of the Xception model and all model weights are set to be trained. The model is fine-tuned over HAM10,000 dataset seven classes by augmentation approach to mitigate the data imbalance effect and conducted a comparative study with the most up to date approaches. In comparison to prior models, the results indicate that the proposed model is both efficient and reliable.

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DEEP LEARNING BASED SKIN LESIONS DIAGNOSIS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w12-81-2019 ◽

2019 ◽

Vol XLII-2/W12 ◽

pp. 81-85 ◽

Cited By ~ 1

Author(s):

D. A. Gavrilov ◽

N. N. Shchelkunov ◽

A. V. Melerzanov

Keyword(s):

Neural Network ◽

Classification Accuracy ◽

Intelligent Systems ◽

State Of The Art ◽

Skin Lesions ◽

Deep Convolutional Neural Networks ◽

Melanoma Diagnosis ◽

Malignant Skin ◽

Diagnosis Accuracy ◽

Visual Diagnosis

<p><strong>Abstract.</strong> Melanoma is one of the most virulent lesions of human’s skin. The visual diagnosis accuracy of melanoma directly depends on the doctor’s qualification and specialization. State-of-the-art solutions in the field of image processing and machine learning allows to create intelligent systems based on artificial convolutional neural network exceeding human’s rates in the field of object classification, including the case of malignant skin lesions. This paper presents an algorithm for the early melanoma diagnosis based on artificial deep convolutional neural networks. The algorithm proposed allows to reach the classification accuracy of melanoma at least 91%.</p>

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Classification Of Skin Lesions By Topological Data Analysis Alongside With Neural Network

10.36227/techrxiv.12628088 ◽

2020 ◽

Author(s):

Naiereh Elyasi ◽

mehdi hosseini moghadam

Keyword(s):

Neural Network ◽

Neural Networks ◽

Data Analysis ◽

Skin Diseases ◽

Persistent Homology ◽

Skin Lesions ◽

Topological Data Analysis ◽

Deep Convolutional Neural Networks ◽

Classification Result

In this paper we use TDA mapper alongside with deep convolutional neural networks in the classification of 7 major skin diseases. First we apply kepler mapper with neural network as one of its filter steps to classify the dataset HAM10000. Mapper visualizes the classification result by a simplicial complex, where neural network can not do this alone, but as a filter step neural network helps to classify data better. Furthermore we apply TDA mapper and persistent homology to understand the weights of layers of mobilenet network in different training epochs of HAM10000. Also we use persistent diagrams to visualize the results of analysis of layers of mobilenet network.

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Deep Convolutional Neural Network based Ship Images Classification

Defence Science Journal ◽

10.14429/dsj.71.16236 ◽

2021 ◽

Vol 71 (2) ◽

pp. 200-208

Author(s):

Narendra Kumar Mishra ◽

Ashok Kumar ◽

Kishor Choudhury

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Classification Accuracy ◽

Data Augmentation ◽

Fine Tuning ◽

Average Classification Accuracy ◽

Maritime Traffic ◽

Proposed Model ◽

High Level ◽

Deep Learning Model

Ships are an integral part of maritime traffic where they play both militaries as well as non-combatant roles. This vast maritime traffic needs to be managed and monitored by identifying and recognising vessels to ensure the maritime safety and security. As an approach to find an automated and efficient solution, a deep learning model exploiting convolutional neural network (CNN) as a basic building block, has been proposed in this paper. CNN has been predominantly used in image recognition due to its automatic high-level features extraction capabilities and exceptional performance. We have used transfer learning approach using pre-trained CNNs based on VGG16 architecture to develop an algorithm that performs the different ship types classification. This paper adopts data augmentation and fine-tuning to further improve and optimize the baseline VGG16 model. The proposed model attains an average classification accuracy of 97.08% compared to the average classification accuracy of 88.54% obtained from the baseline model.

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Classification Of Skin Lesions By Topological Data Analysis Alongside With Neural Network

10.36227/techrxiv.12628088.v1 ◽

2020 ◽

Author(s):

Naiereh Elyasi ◽

mehdi hosseini moghadam

Keyword(s):

Neural Network ◽

Neural Networks ◽

Data Analysis ◽

Skin Diseases ◽

Persistent Homology ◽

Skin Lesions ◽

Topological Data Analysis ◽

Deep Convolutional Neural Networks ◽

Classification Result

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Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Sensors ◽

10.3390/s21082648 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2648

Author(s):

Muhammad Aamir ◽

Tariq Ali ◽

Muhammad Irfan ◽

Ahmad Shaf ◽

Muhammad Zeeshan Azam ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Natural Disasters ◽

Deep Convolutional Neural Network ◽

Multispectral Images ◽

Learning Techniques ◽

Proposed Model ◽

Disaster Intensity ◽

And Performance

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

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Different Forecasting Horizons Based Performance Analysis of Electricity Load Forecasting Using Multilayer Perceptron Neural Network

Forecasting ◽

10.3390/forecast3040049 ◽

2021 ◽

Vol 3 (4) ◽

pp. 804-838

Author(s):

Manogaran Madhiarasan ◽

Mohamed Louzazni

Keyword(s):

Neural Network ◽

Power System ◽

Multilayer Perceptron ◽

Load Forecasting ◽

Consumer Information ◽

Fine Tuning ◽

Atmospheric Parameters ◽

Proposed Model ◽

Electricity Load ◽

Electricity Load Forecasting

With an uninterrupted power supply to the consumer, it is obligatory to balance the electricity generated by the electricity load. The effective planning of economic dispatch, reserve requirements, and quality power provision for accurate consumer information concerning the electricity load is needed. The burden on the power system engineers eased electricity load forecasting is essential to ensure the enhanced power system operation and planning for reliable power provision. Fickle nature, atmospheric parameters influence makes electricity load forecasting a very complex and challenging task. This paper proposed a multilayer perceptron neural network (MLPNN) with an association of recursive fine-tuning strategy-based different forecasting horizons model for electricity load forecasting. We consider the atmospheric parameters as the inputs to the proposed model, overcoming the atmospheric effect on electricity load forecasting. Hidden layers and hidden neurons based on performance investigation performed. Analyzed performance of the proposed model with other existing models; the comparative performance investigation reveals that the proposed forecasting model performs rigorous with a minimal evaluation index (mean square error (MSE) of 1.1506 × 10-05 for Dataset 1 and MSE of 4.0142 × 10-07 for Dataset 2 concern to the single hidden layer and MSE of 2.9962 × 10-07 for Dataset 1, and MSE of 1.0425 × 10-08 for Dataset 2 concern to two hidden layers based proposed model) and compared to the considered existing models. The proposed neural network possesses a good forecasting ability because we develop based on various atmospheric parameters as the input variables, which overcomes the variance. It has a generic performance capability for electricity load forecasting. The proposed model is robust and more reliable.

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Effective Classification of Melting Curve in Real-Time PCR based on Dynamic Filter-based Convolutional Neural Network

Current Bioinformatics ◽

10.2174/1574893616666210212084839 ◽

2021 ◽

Vol 16 ◽

Author(s):

Di Gai ◽

Xuanjing Shen ◽

Haipeng Chen

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Melting Curve ◽

Classification Model ◽

Proposed Model ◽

Unbalanced Dataset ◽

Manual Selection ◽

Low Efficiency ◽

Dynamic Filter

Background: The effective classification of the melting curve is conducive to measure the specificity of the amplified products and the influence of invalid data on subsequent experiments is excluded. Objective: In this paper, a convolutional neural network (CNN) classification model based on dynamic filter is proposed, which can categorize the number of peaks in the melting curve image and distinguish the pollution data represented by the noise peaks. Method: The main advantage of the proposed model is that it adopts the filter which changes with the input and uses the dynamic filter to capture more information in the image, making the network learning more accurate. In addition, the residual module is used to extract the characteristics of the melting curve, and the pooling operation is replaced with an atrous convolution to prevent the loss of context information. Result: In order to train the proposed model, a novel melting curve dataset is created, which includes a balanced dataset and an unbalanced dataset. The proposed method uses six classification-based assessment criteria to compare with seven representative methods based on deep learning. Experimental results show that proposed method is not only markedly outperforms the other state-of-the-art methods in accuracy, but also has much less running time. Conclusion: It evidently proves that the proposed method is suitable for judging the specificity of amplification products according to the melting curve. Simultaneously, it overcomes the difficulties of manual selection with low efficiency and artificial bias.

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Tomato pest classification using deep convolutional neural network with transfer learning, fine tuning and scratch learning

Intelligent Decision Technologies ◽

10.3233/idt-200192 ◽

2021 ◽

pp. 1-10

Author(s):

Gayatri Pattnaik ◽

Vimal K. Shrivastava ◽

K. Parvathi

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Data Augmentation ◽

State Of The Art ◽

Deep Convolutional Neural Network ◽

Fine Tuning ◽

Tomato Plants ◽

Random Weights

Pests are major threat to economic growth of a country. Application of pesticide is the easiest way to control the pest infection. However, excessive utilization of pesticide is hazardous to environment. The recent advances in deep learning have paved the way for early detection and improved classification of pest in tomato plants which will benefit the farmers. This paper presents a comprehensive analysis of 11 state-of-the-art deep convolutional neural network (CNN) models with three configurations: transfers learning, fine-tuning and scratch learning. The training in transfer learning and fine tuning initiates from pre-trained weights whereas random weights are used in case of scratch learning. In addition, the concept of data augmentation has been explored to improve the performance. Our dataset consists of 859 tomato pest images from 10 categories. The results demonstrate that the highest classification accuracy of 94.87% has been achieved in the transfer learning approach by DenseNet201 model with data augmentation.

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A Novel Method for Sea-Land Clutter Separation Using Regularized Randomized and Kernel Ridge Neural Networks

Sensors ◽

10.3390/s20226491 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6491

Author(s):

Le Zhang ◽

Jeyan Thiyagalingam ◽

Anke Xue ◽

Shuwen Xu

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Classification Accuracy ◽

Signal Amplitude ◽

Statistical Characteristics ◽

Support Vector ◽

Amplitude Change ◽

Novel Method

Classification of clutter, especially in the context of shore based radars, plays a crucial role in several applications. However, the task of distinguishing and classifying the sea clutter from land clutter has been historically performed using clutter models and/or coastal maps. In this paper, we propose two machine learning, particularly neural network, based approaches for sea-land clutter separation, namely the regularized randomized neural network (RRNN) and the kernel ridge regression neural network (KRR). We use a number of features, such as energy variation, discrete signal amplitude change frequency, autocorrelation performance, and other statistical characteristics of the respective clutter distributions, to improve the performance of the classification. Our evaluation based on a unique mixed dataset, which is comprised of partially synthetic clutter data for land and real clutter data from sea, offers improved classification accuracy. More specifically, the RRNN and KRR methods offer 98.50% and 98.75% accuracy, outperforming the conventional support vector machine and extreme learning based solutions.

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Intelligent Fault Diagnosis of Rotary Machinery by Convolutional Neural Network with Automatic Hyper-Parameters Tuning Using Bayesian Optimization

Sensors ◽

10.3390/s21072411 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2411

Author(s):

Davor Kolar ◽

Dragutin Lisjak ◽

Michał Pająk ◽

Mihael Gudlin

Keyword(s):

Neural Network ◽

Machine Learning ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Classification Accuracy ◽

Bayesian Optimization ◽

Intelligent Fault Diagnosis ◽

Accelerometer Signal ◽

Rotary Machinery

Intelligent fault diagnosis can be related to applications of machine learning theories to machine fault diagnosis. Although there is a large number of successful examples, there is a gap in the optimization of the hyper-parameters of the machine learning model, which ultimately has a major impact on the performance of the model. Machine learning experts are required to configure a set of hyper-parameter values manually. This work presents a convolutional neural network based data-driven intelligent fault diagnosis technique for rotary machinery which uses model with optimized hyper-parameters and network structure. The proposed technique input raw three axes accelerometer signal as high definition 1-D data into deep learning layers with optimized hyper-parameters. Input is consisted of wide 12,800 × 1 × 3 vibration signal matrix. Model learning phase includes Bayesian optimization that optimizes hyper-parameters of the convolutional neural network. Finally, by using a Convolutional Neural Network (CNN) model with optimized hyper-parameters, classification in one of the 8 different machine states and 2 rotational speeds can be performed. This study accomplished the effective classification of different rotary machinery states in different rotational speeds using optimized convolutional artificial neural network for classification of raw three axis accelerometer signal input. Overall classification accuracy of 99.94% on evaluation set is obtained with the CNN model based on 19 layers. Additionally, more data are collected on the same machine with altered bearings to test the model for overfitting. Result of classification accuracy of 100% on second evaluation set has been achieved, proving the potential of using the proposed technique.

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