TY  - JOUR
AU  - Hamza, Muhammad
AU  - Ali, Ibrar
AU  - Ali, Sikandar
AU  - Khan, Waqas
AU  - Shah, Sayed Mudassir
AU  - Haq, Ijaz Ul
PY  - 2025
DA  - 2025/02/16
TI  - Leveraging Machine Learning and Deep Learning for Advanced Malaria Detection Through Blood Cell Images
JO  - ICCK Journal of Image Analysis and Processing
T2  - ICCK Journal of Image Analysis and Processing
JF  - ICCK Journal of Image Analysis and Processing
VL  - 1
IS  - 1
SP  - 17
EP  - 26
DO  - 10.62762/JIAP.2025.514726
UR  - https://www.icck.org/article/abs/JIAP.2025.514726
KW  - logistic regression
KW  - support vector machine
KW  - Inception-V3
KW  - malaria
KW  - classification
AB  - Malaria remains a significant global health challenge, causing hundreds of thousands of deaths annually, particularly in tropical and subtropical regions. This study proposes an advanced automated approach for malaria detection by classifying red blood cell images using machine learning and deep learning techniques. Three distinct models: Logistic Regression (LR), Support Vector Machine (SVM), and Inception-V3 were implemented and rigorously evaluated on a dataset comprising 27,558 cell images. The LR model achieved an accuracy of 65.38%, while SVM demonstrated improved classification performance with an accuracy of 84%. The deep learning-based Inception-V3 model outperformed both, achieving a classification accuracy of 94.52% after five training epochs, demonstrating its superior capability to extract intricate features from medical images. These results highlight the effectiveness of deep learning architectures in malaria diagnosis and pave the way for scalable, automated solutions, particularly in resource-limited settings.
SN  - 3068-6679
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Hamza2025Leveraging,
  author = {Muhammad Hamza and Ibrar Ali and Sikandar Ali and Waqas Khan and Sayed Mudassir Shah and Ijaz Ul Haq},
  title = {Leveraging Machine Learning and Deep Learning for Advanced Malaria Detection Through Blood Cell Images},
  journal = {ICCK Journal of Image Analysis and Processing},
  year = {2025},
  volume = {1},
  number = {1},
  pages = {17-26},
  doi = {10.62762/JIAP.2025.514726},
  url = {https://www.icck.org/article/abs/JIAP.2025.514726},
  abstract = {Malaria remains a significant global health challenge, causing hundreds of thousands of deaths annually, particularly in tropical and subtropical regions. This study proposes an advanced automated approach for malaria detection by classifying red blood cell images using machine learning and deep learning techniques. Three distinct models: Logistic Regression (LR), Support Vector Machine (SVM), and Inception-V3 were implemented and rigorously evaluated on a dataset comprising 27,558 cell images. The LR model achieved an accuracy of 65.38\%, while SVM demonstrated improved classification performance with an accuracy of 84\%. The deep learning-based Inception-V3 model outperformed both, achieving a classification accuracy of 94.52\% after five training epochs, demonstrating its superior capability to extract intricate features from medical images. These results highlight the effectiveness of deep learning architectures in malaria diagnosis and pave the way for scalable, automated solutions, particularly in resource-limited settings.},
  keywords = {logistic regression, support vector machine, Inception-V3, malaria, classification},
  issn = {3068-6679},
  publisher = {Institute of Central Computation and Knowledge}
}