Ghulam Raza - Academia.edu (original) (raw)

Papers by Ghulam Raza

The Professional Medical Journal

Objective: To compare pitavastatin 4 mg (high dose) with pitavastatin 2 mg (moderate dose) and ez... more Objective: To compare pitavastatin 4 mg (high dose) with pitavastatin 2 mg (moderate dose) and ezetimibe combination on LDL-C in type II diabetics. Study Design: Setting: National Medical Center (NMC) and PNS Shifa Hospital. Period: January and June 2022. Material & Methods: Fifty diagnosed type II diabetics on metformin, 25 in the monotherapy group (group A) and 25 in the combination therapy group (group B). Glycosylated hemoglobin and lipid profile tests were conducted in labs pre therapy and 3 months post therapy. LDL-C levels were recorded and compared. Result: Average LDL-C level drop of 13.51% occurred in the monotherapy group and 17.48% in the combination therapy group. Chi square test showed that LDL-C target levels (< 130 mg/dl) were reached in 33% patients in the monotherapy group and in 64% patients in the combination group. Conclusion: Pitavastatin monotherapy and combination therapy both lowered LDL-C levels, however, combination therapy with ...

International Journal of Management Studies and Social Science Research

Despite many studies on the sustainable tourism, there have been few that focused on the integrat... more Despite many studies on the sustainable tourism, there have been few that focused on the integration of sustainable tourism principles in the tourism planning process and the challenges the local stakeholders are facing in its implementation particularly in the small island province. This study aimed to determine the extent of incorporating the sustainable tourism practices in the tourism planning process and the challenges in faced by the local stakeholders in its implementation in the small island province of Marinduque. The study also aimed to determine the significant relationship of the demographics in their responses and the significant difference between the responses of the major stakeholders involved in tourism planning- (1) government (2) Private sector (3) community. The result of this study revealed that a significant relationship with Age and Status of employment on their perceptions and no significant difference with Sex and Highest Educational Attainment. Significant ...

Computerized Medical Imaging and Graphics

Worldwide, the burden of cancer is drastically increasing over the past few years. Among all type... more Worldwide, the burden of cancer is drastically increasing over the past few years. Among all types of cancers in women, breast cancer (BrC) is the main cause of unnatural deaths. For early diagnosis, histopathology (Hp) imaging is a gold standard for positive and detailed (at tissue level) diagnosis of breast tumor (BrT) compared to mammogram images. A large number of studies used BrT Hp images to solve binary or multiclassification problems using high computational resources. However, classification models' performance may be compromised due to the high correlation among various types of BrT in Hp images, which raises the misclassification rate. Thus, this paper aims to develop a tree-based BrT multiclassification model via deep learning (DL) to extract discriminative features to solve the multiclassification problem with better performance using less computational resources. The main contributions of this work are to create an ensemble, tree-based DL model that is pre-trained on the BreakHis dataset, and implementation of a misclassification reduction algorithm. The ensemble, tree-based DL model, extracts discriminative BrT features from Hp images. The target dataset (i.e., Bioimaging challenge 2015 breast histology) is small in size; thus, to avoid overfitting of the proposed model, pretraining is performed on the BreakHis dataset. Whereas, misclassification reduction algorithm is implemented to enhance the performance of the classification model. The experimental results show that the proposed model outperformed the existing state-of-the-art baseline studies. The achieved classification accuracy is ranging from 87.50 % to 100 % for four subtypes of BrT. Thus, the proposed model can assist doctors as the second opinion in any healthcare centre.

Radiotherapy and Oncology, 2020

Sukkur IBA Journal of Computing and Mathematical Sciences, 2021

Globally, breast cancer (BC) is the prevailing cause of unusual deaths in women. Breast tumor (BT... more Globally, breast cancer (BC) is the prevailing cause of unusual deaths in women. Breast tumor (BT) is a primary symptom and may lead to BC. Digital histology (DH) image modality is a gold standard medical test for a definite diagnosis of BC. Traditionally, DH images are visually examined by two or more pathologists to come up with a consensus for authentic BC detection which may cause a high error rate. Therefore, researchers had developed automated BC detection models using a machine learning (ML) based approach. Thus, this study aims to develop a BC detection model through ten feature extraction methods which extract both local and global type features from publicly available breast histology dataset. The extracted features are sorted by their weights, which are computed by the neighborhood component analysis method. A feature selection algorithm is developed to find the minimum number of discriminating features, evaluated through seven heterogeneous traditional ML classifiers. Th...

Multimedia Tools and Applications, 2020

Breast cancer (BrC) is the leading cause of abnormal death in women. Mammograms and histopatholog... more Breast cancer (BrC) is the leading cause of abnormal death in women. Mammograms and histopathology (Hp) biopsy images are generally recommended for early diagnosis of BrC because Hp image-based diagnosis enables doctors to make cancer diagnostic decisions more confidently than with mammograms. Several studies have used Hp images to classify BrC. However, the performance of classification models is compromised due to the higher misclassification rate. Therefore, this study aimed to develop a reliable, accurate, and computationally cost-effective ensembled BrC classification network (EBrC-Net) model with three misclassification algorithms to diagnose breast malignancy in early stages using Hp images. The proposed EBrC-Net model is based on the deep convolutional neural network approach. For experiments, the publicly available BreakHis dataset was used and split into training, validation, and testing sets. In addition, image augmentation was adopted for the training set only, and features were extracted through the well-trained EBrC-Net. Thereafter, the extracted features were further evaluated by six machine learning classifiers, of which two best performing classifiers (i.e., softmax and k-nearest neighbour [kNN]) were selected on the basis of five performance metric evaluation results. Furthermore, three misclassification reduction (McR) algorithms were developed and implemented in cascaded manner to reduce the false predictions of the softmax and kNN classifiers. After the implementation of the McR algorithms, experiments showed that the kNN results were much better and reliable than the softmax. The proposed BrC classification model achieved accuracy, specificity, and sensitivity rates of 97.74%, 100%, and 97.01%, respectively. Moreover, the performance of proposed BrC classification model was compared with that of state-of-the-art baseline models. Findings showed that the proposed EBrC-Net classification model, coupled with Multimedia Tools and Applications

Artificial Intelligence Review, 2019

Breast cancer is a common and fatal disease among women worldwide. Therefore, the early and preci... more Breast cancer is a common and fatal disease among women worldwide. Therefore, the early and precise diagnosis of breast cancer plays a pivotal role to improve the prognosis of patients with this disease. Several studies have developed automated techniques using different medical imaging modalities to predict breast cancer development. However, few review studies are available to recapitulate the existing literature on breast cancer classification. These studies provide an overview of the classification, segmentation, or grading of many cancer types, including breast cancer, by using traditional machine learning approaches through handengineered features. This review focuses on breast cancer classification by using medical imaging multimodalities through state-of-the-art artificial deep neural network approaches. It is anticipated to maximize the procedural decision analysis in five aspects, such as types of imaging modalities, datasets and their categories, pre-processing techniques, types of deep neural network, and performance metrics used for breast cancer classification. Forty-nine journal and conference publications from eight academic repositories were methodically selected and carefully reviewed from the perspective of the five aforementioned aspects. In addition, this study provided quantitative, qualitative, and critical analyses of the five aspects. This review showed that mammograms and histopathologic images were mostly used to classify breast cancer. Moreover, about 55% of the selected studies used public datasets, and the remaining used exclusive datasets. Several studies employed augmentation, scaling, and image normalization pre-processing techniques to minimize inconsistencies in breast cancer images. Several types of shallow and deep neural network architecture were employed to classify breast cancer using images. The convolutional neural network was utilized frequently to construct an effective breast cancer classification model. Some of the selected studies employed a pre-trained network or developed new deep neural networks to classify breast cancer. Most of the selected studies used accuracy and area-under-the-curve metrics followed B Ghulam Murtaza

Journal of Physics: Conference Series, 2019

Breast cancer (BC) infection, which is peculiar to women, brings about the high rate of deaths am... more Breast cancer (BC) infection, which is peculiar to women, brings about the high rate of deaths among women in every part of the world. The early investigation of BC has minimized the severe effects of cancer as compared to the last stage diagnosis. Doctors for diagnostic tests usually suggest the medical imaging modalities like mammograms or biopsy histopathology (Hp) images. However, Hp image analysis gives doctors more confidence to diagnose BC as compared to mammograms. Many studies used Hp images to develop BC classification models to assist doctors in early BC diagnosis. However, these models lack better and reliable results in terms of reporting multiple performance evaluation metrics. Therefore, the goal of this study is to create a reliable, more accurate model that consumes minimum resources by using transfer learning based convolution neural network model. The proposed model uses the trained model after fine tuning, hence requires less number of images and can show better ...

Multimedia Tools and Applications, 2019

Breast cancer (BC) is the third leading cause of deaths in women globally. In general, histopatho... more Breast cancer (BC) is the third leading cause of deaths in women globally. In general, histopathology images are recommended for early diagnosis and detailed analysis for BC. Thus, state-of-the-art classification models are required for the early prediction of BC using histopathology images. This study aims to develop an accurate and computationally feasible classification model named Biopsy Microscopic Image Cancer Network (BMIC_Net) to classify BC into eight distinct subtypes through deep learning (DL) and hierarchical classification approach. For experiments, the publicly available dataset BreakHis is used and splitted into training and testing set. Furthermore, data augmentation was performed on training set only and 4096 result-oriented features were extracted through DL. In order to improve the classification performance, feature reduction schemes were experimented to elicit the most discriminative feature subset. Finally, six machine-learning algorithms were analyzed to acquire the best results. The experimental results revealed that BMIC_Net outperformed existing baseline models by obtaining the highest accuracy of 95.48% for first-level classifier and 94.62% and 92.45% for second-level classifiers. Thus, this model can be deployed on a normal desktop machine in any healthcare center of less privileged areas in under-developing countries to serve as second opinion for breast cancer classification.

Medicinal & Aromatic Plants, 2018

Pakistan is gifted with peculiar resources of rich and diversified plant heritage. Mountainous re... more Pakistan is gifted with peculiar resources of rich and diversified plant heritage. Mountainous regions (Northern part) of Pakistan have more than 700 plants of economic importance. Of which 10% plant species are reflected to be of medicinal value. Active chemical constituents present either with in stem, bark, leaves, flowers, fruits, seeds or roots are used for different ailments. These are chemically balanced, effective and least injurious for human health with fewer side effects. The increasing human and livestock's population change in climatic condition, habitat loss through housing, construction of roads, promotion of tourism, deforestation, terracing of land for agriculture, overgrazing and excessive collection of plants has exerted pressure on the existence of rare medicinal plants. The herbal medicines are mostly being used in the form of crude extract and their standardization and quality has remained one of the key challenges. The convenient standardization of bioactive compounds present in medicinal plants is becoming trendy at present. There is a terrible need of conservation of medicinally important plants via modern biotechnological approaches for their long-term sustainable utilization.

Current Drug Target -CNS & Neurological Disorders, 2005