Adaptive Aquila Optimizer with Explainable Artificial Intelligence-Enabled Cancer Diagnosis on Medical Imaging

Salem Alkhalaf; Fahad Alturise; Adel Aboud Bahaddad; Bushra M Elamin Elnaim; Samah Shabana; Sayed Abdel-Khalek; Romany F Mansour

doi:10.3390/cancers15051492

Adaptive Aquila Optimizer with Explainable Artificial Intelligence-Enabled Cancer Diagnosis on Medical Imaging

Cancers (Basel). 2023 Feb 27;15(5):1492. doi: 10.3390/cancers15051492.

Authors

Salem Alkhalaf¹, Fahad Alturise¹, Adel Aboud Bahaddad², Bushra M Elamin Elnaim³, Samah Shabana⁴, Sayed Abdel-Khalek⁵, Romany F Mansour⁶

Affiliations

¹ Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass 58892, Saudi Arabia.
² Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
³ Department of Computer Science, College of Science and Humanities in Al-Sulail, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia.
⁴ Pharmacognosy Department, Faculty of Pharmaceutical Sciences and Drug Manufacturing, Misr University for Science and Technology (MUST), Giza 3236101, Egypt.
⁵ Department of Mathematics, College of Science, Taif University, Taif 21944, Saudi Arabia.
⁶ Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 1064188, Egypt.

Abstract

Explainable Artificial Intelligence (XAI) is a branch of AI that mainly focuses on developing systems that provide understandable and clear explanations for their decisions. In the context of cancer diagnoses on medical imaging, an XAI technology uses advanced image analysis methods like deep learning (DL) to make a diagnosis and analyze medical images, as well as provide a clear explanation for how it arrived at its diagnoses. This includes highlighting specific areas of the image that the system recognized as indicative of cancer while also providing data on the fundamental AI algorithm and decision-making process used. The objective of XAI is to provide patients and doctors with a better understanding of the system's decision-making process and to increase transparency and trust in the diagnosis method. Therefore, this study develops an Adaptive Aquila Optimizer with Explainable Artificial Intelligence Enabled Cancer Diagnosis (AAOXAI-CD) technique on Medical Imaging. The proposed AAOXAI-CD technique intends to accomplish the effectual colorectal and osteosarcoma cancer classification process. To achieve this, the AAOXAI-CD technique initially employs the Faster SqueezeNet model for feature vector generation. As well, the hyperparameter tuning of the Faster SqueezeNet model takes place with the use of the AAO algorithm. For cancer classification, the majority weighted voting ensemble model with three DL classifiers, namely recurrent neural network (RNN), gated recurrent unit (GRU), and bidirectional long short-term memory (BiLSTM). Furthermore, the AAOXAI-CD technique combines the XAI approach LIME for better understanding and explainability of the black-box method for accurate cancer detection. The simulation evaluation of the AAOXAI-CD methodology can be tested on medical cancer imaging databases, and the outcomes ensured the auspicious outcome of the AAOXAI-CD methodology than other current approaches.

Keywords: Adaptive Aquila Optimizer; cancer diagnosis; deep learning; ensemble learning; explainable artificial intelligence.

Grants and funding

This research received no external funding.