Department of Computer Science

Permanent URI for this communityhttps://repository.cuilahore.edu.pk/handle/123456789/16

Browse

Author: search.filters.author.Dr. Allah Bux SarganoSubject: search.filters.subject.Sp19

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    A Machine Learning Approach for Mutation Identification in Gene Sequences to Detect Lungs Cancer
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Umer Farooq; SP19-RCS-029; LHR TP 8334; Dr. Allah Bux Sargano
    Cancer is one of the leading causes of death worldwide. The mutation is one of the basic causes of cancer, which is caused by any change in the gene sequences of the human body. The abnormal and uncontrolled growth in the cells of the lungs is a cause of lung cancer. It is the second most common type of cancer worldwide, considered 11.2% of all cancers. According to the cancer statistics of the United States of America (USA), about 236,740 people were diagnosed with lung cancer in the year 2021, and about 130,180 people died from lung cancer in the same year. This study aims to develop a dataset, machine learning, and deep learning-based techniques to address the above-mentioned issues. In this regard, the asia.ensembl.org website is used to extract normal gene sequences from the lung cancer dataset, while the mutated gene sequences for lung cancer are obtained from the intogen.org website. Thus, the dataset of 1588 samples consisting of 2576 mutations is developed by following the standard dataset creation method. Then, different feature extraction methods are applied to extract the discriminative features from the dataset; these features include raw moment, Hahn moment, central moment, position relative incident matrix (PRIM), reverse position relative incident matrix (RPRIM) calculation, feature vector determination, accumulative absolute position incidence vector (AAPIV), reverse accumulative absolute position incidence vector (RAAPIV). These features are then fed to the traditional machine learning (ML) algorithms such as Random Forest (RF), Logistic Regression (LR), Multi-Layer Perceptron (MLP), and an ensemble of these traditional ML algorithms. These extracted features are also fed to deep learning (DL) methods such as LSTM, GRU, Bi-LSTM, and an ensemble of these DL algorithms. An averaging technique is applied to ensemble the classification algorithms. Three validation methods are used for evaluation: self consistency testing, 10-fold cross-validation, and independent set testing. The final classification result of lung cancer is generated as accuracy, specificity, Mathew's Correlation Coefficient (MCC), sensitivity, and Cohen's Kappa. The efficacy of this study can be measured with the accuracy of 94.5% obtained from the ensemble of x machine learning models using 10-FCV and with the accuracy of 97.3% obtained from the ensemble of deep learning models using the independent set test.
  • No Thumbnail Available
    Item
    Super-Resolution of Medical Images using CNN
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Maha Anis; SP19-RCS-023; LHR TP 7288; Dr. Allah Bux Sargano
    Super-resolution is a technique that improves the quality of an image. Super-resolution images contain more detail because they have a high density of pixels. Super-resolution images provide complementary detail for clinical diagnosis, astronomy, and biometry. The medical images should be of good resolution for perceiving fine-grained details in the image so that the patients are diagnosed at their early stages of tumours. If the images have low resolution, doctors cannot diagnose the tumours or segment the malignant regions. For super-resolution images, the cost in terms of machines and the health of patients also matter. If patients are at high risk of cancerous cells, continuous radiation exposure will lead to activation of these cancerous cells. So the goal is to enhance the low-quality scans. This thesis emphasises single image super-resolution (SISR). Previously used techniques for the task of super-resolution were interpolation and reconstruction. However, these techniques have limitations in the loss of information in images like edges and boundaries. Recently, deep learning was introduced and aimed to features extraction from the related images for enhancement automatically. For this purpose, Convolutional Neural Networks (CNNs) are employed due to their excellent results in similar domains. These CNN based approaches achieved encouraging results. Transfer learning have been successfully used for image recognition and human action detection. This research work foremostly in-depth examined the learned representation models incorporating transfer learning and fusion of different models. EDSR and WDSR are the winners of NTIRE challenges for the super resolution of natural images. These models, including SRGAN, are evaluated with and without transfer learning. The proposed concepts unleash the integrated potential of these three models in the medical domain with higher PSNR and SSIM as compared to contemporary models. The transfer learning EDSR, WDSR, and SRGAN achieved 40.38, 39.02, and 38.15 PSNR and 0.964, 0.963, and 0.953 SSIM respectively. The fusion of these three models shows PSNR, and SSIM 37.84 and 0.964 respectively. The results report that proposed approaches outperformed state-of-the-art methods by learning super resolution features on NAMIC datasets. The results highlight the importance of transfer learning