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Results for “"Inam Ullah Khan"”

13 results

A Lightweight Robust Deep Learning Model Gained High Accuracy in Classifying a Wide Range of Diabetic Retinopathy Images

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Mohaimenul Azam Khan Raiaan, Kaniz Fatema, Inam Ullah Khan, Sami Azam et al.

Journal: IEEE AccessYear: 2023Citations: 99

Diabetic retinopathy (DR) is a common complication of diabetes mellitus, and retinal blood vessel damage can lead to vision loss and blindness if not recognized at an early stage. Manual DR detection using large fundus image data is time-consuming and error-prone. An effective automatic DR detection...

Health SciencesMedicineRadiology, Nuclear Medicine and ImagingOpen Access
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A Novel Hybrid Deep Learning Model for Metastatic Cancer Detection

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Shahab Ahmad, Tahir Ullah, Ijaz Ahmad, Abdulkarem AL-Sharabi et al.

Journal: Computational Intelligence and NeuroscienceYear: 2022Citations: 77

Cancer has been found as a heterogeneous disease with various subtypes and aims to destroy the body's normal cells abruptly. As a result, it is essential to detect and prognosis the distinct type of cancer since they may help cancer survivors with treatment in the early stage. It must also divide ca...

Physical SciencesComputer ScienceArtificial IntelligenceOpen Access
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Diagnosis of Diabetic Retinopathy through Retinal Fundus Images and 3D Convolutional Neural Networks with Limited Number of Samples

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Ahsan Bin Tufail, Inam Ullah, Wali Ullah Khan, Muhammad Asif et al.

Journal: Wireless Communications and Mobile ComputingYear: 2021Citations: 61

Diabetic retinopathy (DR) is a worldwide problem associated with the human retina. It leads to minor and major blindness and is more prevalent among adults. Automated screening saves time of medical care specialists. In this work, we have used different deep learning (DL) based 3D convolutional neur...

Health SciencesMedicineRadiology, Nuclear Medicine and ImagingOpen Access
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SkinNet-14: a deep learning framework for accurate skin cancer classification using low-resolution dermoscopy images with optimized training time

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Abdullah Al Mahmud, Sami Azam, Inam Ullah Khan, Sidratul Montaha et al.

Journal: Neural Computing and ApplicationsYear: 2024Citations: 39

Abstract The increasing incidence of skin cancer necessitates advancements in early detection methods, where deep learning can be beneficial. This study introduces SkinNet-14, a novel deep learning model designed to classify skin cancer types using low-resolution dermoscopy images. Unlike existing m...

Health SciencesMedicineOncologyOpen Access
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A Computer-Aided Diagnostic System to Identify Diabetic Retinopathy, Utilizing a Modified Compact Convolutional Transformer and Low-Resolution Images to Reduce Computation Time

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Inam Ullah Khan, Mohaimenul Azam Khan Raiaan, Kaniz Fatema, Sami Azam et al.

Journal: BiomedicinesYear: 2023Citations: 38

Diabetic retinopathy (DR) is the foremost cause of blindness in people with diabetes worldwide, and early diagnosis is essential for effective treatment. Unfortunately, the present DR screening method requires the skill of ophthalmologists and is time-consuming. In this study, we present an automate...

Health SciencesMedicineRadiology, Nuclear Medicine and ImagingOpen Access
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Fast and Efficient Lung Abnormality Identification With Explainable AI: A Comprehensive Framework for Chest CT Scan and X-Ray Images

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Md. Zahid Hasan, Sidratul Montaha, Inam Ullah Khan, Md. Mehedi Hassan et al.

Journal: IEEE AccessYear: 2024Citations: 18

A novel automated multi-classification approach is proposed for the anticipation of lung abnormalities using chest X-ray and CT images. The study leverages a publicly accessible dataset with an insufficient and unbalanced number of images, addressing this issue by employing the data augmentation app...

Health SciencesMedicineRadiology, Nuclear Medicine and ImagingOpen Access
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An effective approach to address processing time and computational complexity employing modified CCT for lung disease classification

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Inam Ullah Khan, Sami Azam, Sidratul Montaha, Abdullah Al Mahmud et al.

Journal: Intelligent Systems with ApplicationsYear: 2022Citations: 16

Early identification and adequate treatment can help prevent lung disorders from becoming chronic, severe, and life-threatening. X-ray images are commonly used and an automated and effective method involving deep learning techniques can potentially contribute to quick and accurate diagnosis of lung ...

Health SciencesMedicineRadiology, Nuclear Medicine and ImagingOpen Access
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Deep learning-based analysis of COVID-19 X-ray images: Incorporating clinical significance and assessing misinterpretation

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Md. Rahad Islam Bhuiyan, Sami Azam, Sidratul Montaha, Risul Islam Jim et al.

Journal: Digital HealthYear: 2023Citations: 15

COVID-19, pneumonia, and tuberculosis have had a significant effect on recent global health. Since 2019, COVID-19 has been a major factor underlying the increase in respiratory-related terminal illness. Early-stage interpretation and identification of these diseases from X-ray images is essential to...

Health SciencesMedicineRadiology, Nuclear Medicine and ImagingOpen Access
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A Low Complexity Efficient Deep Learning Model for Automated Retinal Disease Diagnosis

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Sadia Sultana Chowa, Md. Rahad Islam Bhuiyan, Israt Jahan Payel, Asif Karim et al.

Journal: Journal of Healthcare Informatics ResearchYear: 2025Citations: 10

The identification and early treatment of retinal disease can help to prevent loss of vision. Early diagnosis allows a greater range of treatment options and results in better outcomes. Optical coherence tomography (OCT) is a technology used by ophthalmologists to detect and diagnose certain eye con...

Health SciencesMedicineRadiology, Nuclear Medicine and ImagingOpen Access
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Optimizing Machine Learning Classifiers for Credit Card Fraud Detection on Highly Imbalanced Datasets Using PCA and SMOTE Techniques

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Zeeshan Ali Haider, Fida Muhammad Khan, Abu Zafar, Nabila et al.

Journal: VAWKUM Transactions on Computer SciencesYear: 2024Citations: 9

Card fraud detection refers to the process of identifying unauthorized or suspicious transactions made using credit or debit cards. It employs machine learning models, rule-based systems, and anomaly detection techniques to detect patterns indicating potential fraud. There is a growing need for syst...

Physical SciencesComputer ScienceArtificial IntelligenceOpen Access
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Diversity-oriented dynamic ensemble selection approach for multi-class road traffic injury severity predictions with interpretable insights

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Kamran Aziz, Feng Chen, Inamullah Khan, Zahid Ullah et al.

Journal: Transportmetrica A Transport ScienceYear: 2025Citations: 3
Physical SciencesEngineeringSafety, Risk, Reliability and QualityOpen Access
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A Robust Deep Learning based Framework for High-Precision Detection of Liver Disease

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Abdullah Al Mahmud, Asif Karim, Inam Ullah Khan, Pronab Ghosh et al.

Year: 2022Citations: 1

Globally, chronic liver disease is a significant cause of death, affecting a large number of people. The liver can be damaged by several factors. Obesity, undiagnosed hepatitis, and alcohol abuse, to name a few examples. This is the cause of inappropriate nerve function, blood in the cough or vomit,...

Health SciencesMedicineEpidemiology
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Efficacy and Safety of Minocycline-Containing Bismuth Quadruple Therapies Versus Standard First-Line Bismuth Quadruple Therapies for Helicobacter pylori Eradication: A Systematic Review and Meta-Analysis

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Hakim Ullah Wazir, Abdul Muqeet Khuram, I M Khalid Reza, Hafsa Ajmal et al.

Journal: Infectious Disease ReportsYear: 2026

Background: Growing antibiotic resistance and the limited availability of key components in standard Helicobacter pylori treatments have driven the search for effective alternatives. Minocycline, with its broad-spectrum activity and favorable pharmacokinetics, has emerged as a promising substitute. ...

Health SciencesMedicineSurgeryOpen Access
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