Automatic Skin Lesion Segmentation and Melanoma Detection: Transfer Learning Approach with U-Net and DCNN-SVM (First author)
Abstract: Industrial pollution resulting in ozone layer depletion has influenced increased UV radiation in recent years which is a major environmental risk factor for invasive skin cancer, melanoma, and other keratinocyte cancers. The incidence of deaths from melanoma has risen worldwide in the past two decades. Deep learning has been employed successfully for dermatologic diagnosis. In this work, we present a deep learning-based scheme to automatically segment skin lesions and detect melanoma from dermoscopy images. U-Net was used for segmenting out the lesion from surrounding skin. The limitation of utilizing deep neural networks with limited medical data was solved with data augmentation and transfer learning. In our experiments, U-Net was used with spatial dropout to solve the problem of overfitting, and different augmentation effects were applied to the training images to increase data samples. The model was evaluated on two different datasets. It achieved a mean dice score of 0.87 and a mean Jaccard index of 0.80 on ISIC 2018 dataset. The trained model was assessed on PH2 dataset where it achieved a mean dice score of 0.93 and a mean Jaccard index of 0.87 with transfer learning. For classification of malignant melanoma, a DCNN-SVM model was used where we compared state-of-the-art deep nets as feature extractors to find the applicability of transfer learning in dermatologic diagnosis domain. Our best model achieved a mean accuracy of 92% on PH2 dataset. The findings of this study are expected to be useful in cancer diagnosis research.
Conference: IJCCI (2018)
Springer Book Series: Algorithms for Intelligent Systems
Full Manuscript: at RGate
Classification of Motor Imagery EEG Signals with multi-input Convolutional Neural Network by augmenting STFT (Second author)
Abstract: Motor imagery EEG classification is a crucial task in the Brain Computer Interface (BCI) system. In this paper, we propose a Motor Imagery EEG signal classification framework based on Convolutional Neural Network (CNN) to inhance the classification accuracy. For the classification of 2 class motor imagery signals, firstly we apply Short Time Fourier Transform (STFT) on EEG time series signals to transform signals into 2D images. Next, we train our proposed multi-input convolutional neural network with feature concatenation to achieve robust classification from the images. Batch normalization is added to regularize the network. Data augmentation is used to increase samples and as a secondary regularizer. A three input CNN was proposed to feed the three channel EEG signals. In our work, the dataset of EEG signal collected from BCI Competition IV dataset 2b and dataset III of BCI Competition II were used. Experimental results show that average classification accuracy achieved was 89.19% on dataset 2b, whereas our model achieved the best performance of 97.7% accuracy for subject 7 on dataset III. We also extended our approach and explored a transfer learning based scheme with pre-trained ResNet-50 model which showed promising result. Overall, our approach showed competitive performance when compared with other methods.
Conference: ICAEE, 2019 [Accepted]
Full Manuscript: at RGate
Bangla Handwritten Digit Recognition Approach with an Ensemble of Deep Residual Networks (Second author)
Abstract: This work presents an Xception ensemble network based Bangla handwritten digit classification scheme. Bangla handwritten digits are challenging to recognize due to some strong similar features between different classes. In this study, heavy augmentation has been used in the training set along with dropout in the model to avoid overfitting. Competitive performance has been achieved with optimized number of model parameters. An ensemble of three Xception networks was evaluated on a hidden test set where it showed promising performance of 96.69% accuracy, F1 score of 97.14%.
Conference: ICBSLP, 2018
Kaggle Competition: Numta, Bengali Handwritten Digit Recognition Challenge (7th Place)
Abstract: Wireless sensor network (WSN) is used to collect physical information from the environment at real time. The information may be temperature, humidity and air pressure. In modern days, the huge number of wireless sensors are distributed in the physical environment. So, the proper power management scheme is necessary for WSN. Interestingly, by using prediction algorithms in the literature, we can predict the future data and compare the predicted data with actual information. In this approach, if the absolute value is within the threshold value, then we can save power by not sending the actual measurements to the base station as the base station is already equipped with similar data prediction algorithm. Previous works are done on this problem by using Simpson 3/8 method and Kalman filter algorithm. Unfortunately, they are not very efficient when the threshold value is small. To maximize the power savings for smart sensors, we are proposing a Milne Simpsons algorithm for prediction and estimation of the transmitted signals. With this method, the prediction accuracy is higher than existing methods. With our proposed approach, the data prediction accuracy rate will be high, resulting in low power consumption in wireless networks.
Conference: ICAEE, 2017
Classification of EEG signals for Brain Computer Interface (BCI) has great impact on people having various kinds of physical disabilities. Motor Imagery (MI) EEG signals of hand and leg movement classification can help people whose limbs are replaced by prosthetics. In this paper, Random Subspace Ensemble (RSE) method has been proposed for improving prediction accuracy of motor imagery EEG signal classification. The method has been tested on four different subjects and a hybrid dataset of two subjects’ data combined. Principal Component Analysis (PCA) has been used for dimensionality reduction of the feature space. A comparative analysis has been carried out where RSE method outperformed other classification models. Furthermore, the model showed better performance with reduced feature set generated by PCA. The maximum accuracy obtained was 95.8% with original feature dimension and 87.5% with PCA features. The findings of this study will contribute to the BCI research.
Conference: ICIEV (Japan), 2018 [Accepted]
Abstract: Information collection from remote location is very important for several tasks such as temperate monitoring, air quality investigation, and wartime surveillance. Wireless sensor network is the first choice to complete these types of tasks. Basically, information prediction scheme is an important feature in any sensor nodes. The efficiency of the sensor network can be improved to large extent with a suitable information prediction scheme. Previously, there were several efforts to resolve this problem, but their accuracy is decreased as the prediction threshold reduces to a small value. Our proposed Adams-Bashforth-Moulton algorithm to overcome this drawback was compared with the Milne Simpson scheme. The proposed algorithm is simulated on distributed sensor nodes where information is gathered from the Intel Berkeley Research Laboratory. To maximize the power saving in wireless sensor network, our adopted method achieves the accuracy of 60.28 and 59.2238 for prediction threshold of 0.01 for Milne Simpson and Adams-Bashforth-Moulton algorithms, respectively.
Journal: Journal of Sensor Technology, 2018
Publisher: Scientific Research Publishing
- Motor Imagery EEG Classification Using Random Subspace Ensemble Network with Variable Length Feature Sampling (submitted)
- Invasive Ductal Carcinoma Detection by Gated Recurrent Unit Network with Self Attention (submitted)
- Classification of ECG signals by dot Residual LSTM Network with data augmentation for anomaly detection (submitted)
- Convolutional Neural Network based approach for pneumonia detection on chest X-ray images (submitted)
- Residual Net for Car Detection with spatial transformation (submitted)
- TagOJ: Tagging Algorithmic Problem Statements from Online Judges with Convolutional Neural Network (submitted)