Biology subjects

Ratul, M. A. R.

Publications and source records attributed to Ratul, M. A. R..

3 recordsLinked to original sources

bioRxiv2019

Prediction of 8-state protein secondary structures by 1D-Inception and BD-LSTM

Protein secondary structure is crucial to create an information bridge between the primary structure and the tertiary (3D) structure. Precise prediction of 8-state protein secondary structure (PSS) significantly utilized in the structural and functional analysis of proteins in bioinformatics. In this recent period, deep learning techniques have been applied in this research area and raise the Q8 accuracy remarkably. Nevertheless, from a theoretical standpoint, there still lots of room for improvement, specifically in 8-state (Q8) protein secondary structure prediction. In this paper, we presented two deep learning architecture, namely 1D-Inception and BD-LSTM, to improve the performance of 8-classes PSS prediction. The input of these two architectures is a carefully constructed feature matrix from the sequence features and profile features of the proteins. Firstly, 1D-Inception is a Deep convolutional neural network-based approach that was inspired by the InceptionV3 model and containing three inception modules. Secondly, BD-LSTM is a recurrent neural network model which including bidirectional LSTM layers. Our proposed 1D-Inception method achieved 76.65%, 71.18%, 76.86%, and 74.07% Q8 accuracy respectively on benchmark CullPdb6133, CB513, CASP10, and CASP11 datasets. Moreover, BD-LSTM acquired 74.71%, 69.49%, 74.07%, and 72.37% state-8 accuracy after evaluated on CullPdb6133, CB513, CASP10, and CASP11 datasets, respectively. Both these architectures enable the efficient processing of local and global interdependencies between amino acids to make an accurate prediction of each class is very beneficial in the deep neural network. To the best of our knowledge, experiment results of the 1D-Inception model demonstrate that it outperformed all the state-of-art methods on the benchmark CullPdb6133, CB513, and CASP10 datasets.

bioinformatics↗

bioRxiv2019

Skin Lesions Classification Using Deep Learning Based on Dilated Convolution

The prediction of skin lesions is a challenging task even for experienced dermatologists due to a little contrast between surrounding skin and lesions, the visual resemblance between skin lesions, fuddled lesion border, etc. An automated computer-aided detection system with given images can help clinicians to prognosis malignant skin lesions at the earliest time. Recent progress in deep learning includes dilated convolution known to have improved accuracy with the same amount of computational complexities compared to traditional CNN. To implement dilated convolution, we choose the transfer learning with four popular architectures: VGG16, VGG19, MobileNet, and InceptionV3. The HAM10000 dataset was utilized for training, validating, and testing, which contains a total of 10015 dermoscopic images of seven skin lesion classes with huge class imbalances. The top-1 accuracy achieved on dilated versions of VGG16, VGG19, MobileNet, and InceptionV3 is 87.42%, 85.02%, 88.22%, and 89.81%, respectively. Dilated InceptionV3 exhibited the highest classification accuracy, recall, precision, and f-1 score and dilated MobileNet also has high classification accuracy while having the lightest computational complexities. Dilated InceptionV3 achieved better overall and per-class accuracy than any known methods on skin lesions classification to the best of our knowledge while experimenting with a complex open-source dataset with class imbalances.

cancer biology↗

bioRxiv2019

Atrous Convolution with transfer learning for Skin Lesions Classification

Skin cancer is a crucial public health issue and by far the most usual kind of cancer specifically in the region of North America. It is estimated that in 2019, only because of melanoma nearly 7,230 people will die, and 192,310 cases of malignant melanoma will be diagnosed. Nonetheless, nearly all types of skin lesions can be treatable if they can be diagnosed at an earlier stage. The accurate prediction of skin lesions is a critically challenging task even for vastly experienced clinicians and dermatologist due to a little distinction between surrounding skin and lesions, visual resemblance between melanoma and other skin lesions, fuddled lesion border, etc. A well-grounded automated computer-aided skin lesions detection system can help clinicians immensely to prognosis malignant skin lesion in the earliest possible time. From the past few years, the emergence of machine learning and deep learning in the medical imaging has produced several image-based classification systems in the medical field and these systems perform better than traditional image processing classification methods. In this paper, we proposed a popular deep learning technique namely atrous or, dilated convolution for skin lesions classification, which is known to have enhanced accuracy with the same amount of computational cost compared to traditional CNN. To implement atrous convolution we choose the transfer learning technique with several popular deep learning architectures such as VGG16, VGG19, MobileNet, and InceptionV3. To train, validate, and test our proposed models we utilize HAM10000 dataset which contains total 10015 dermoscopic images of seven different skin lesions (melanoma, melanocytic nevi, Basal cell carcinoma, Benign keratosis-like lesions, Dermatofibroma, Vascular lesions, and Actinic keratoses). Four of our proposed dilated convolutional frameworks show promising outcome on overall accuracy and per-class accuracy. For example, overall test accuracy achieved 87.42%, 85.02%, 88.22%, and 89.81% on dilated VGG16, dilated VGG19, dilated MobileNet, and dilated IncaptionV3 respectively. These dilated convolutional models outperformed existing networks in both overall accuracy and individual class accuracy. Among all the architectures dilated InceptionV3 shows superior classification accuracy and dilated MobileNet is also achieving almost impressive classification accuracy like dilated InceptionV3 with the lightest computational complexities than all other proposed model. Compared to previous works done on skin lesions classification we have experimented one of the most complicated open-source datasets with class imbalances and achieved better accuracy (dilated inceptionv3) than any known methods to the best of our knowledge.

cancer biology↗