article · Cancers
This study developed an Adaptive Aquila Optimizer with Explainable Artificial Intelligence Enabled Cancer Diagnosis (AAOXAI-CD) technique for classifying colorectal and osteosarcoma cancers from medical images. The AAOXAI-CD system uses a Faster SqueezeNet model for generating feature vectors, with its hyperparameters tuned by an Adaptive Aquila Optimizer (AAO) algorithm. For cancer classification, it employs an ensemble model combining three deep learning classifiers: recurrent neural network (RNN), gated recurrent unit (GRU), and bidirectional long short-term memory (BiLSTM). To enhance transparency and understanding of its decisions, the technique integrates the LIME explainable AI approach. Simulation evaluations on medical cancer imaging databases showed promising results compared to existing methods.
This research is important because it aims to improve the accuracy and trustworthiness of AI-driven cancer diagnoses. By explaining how an AI system arrives at its conclusions, it can help both doctors and patients better understand and accept the diagnostic process, potentially leading to more confident and timely treatment decisions.
This early-stage research could lead to advanced diagnostic tools for medical professionals, specifically for colorectal and osteosarcoma cancer detection. The explainable AI component could build trust among clinicians and patients, facilitating adoption in clinical settings. Further development and validation beyond simulation would be needed to move towards real-world application in hospitals and diagnostic centres.
AI-generated from the published abstract. Always read the original work before citing.
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.
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DOI: 10.3390/cancers15051492
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