# A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging  
[PERSON]  
1 Computer Science & Engineering Department, J.B. Speed School of Engineering, University of Louisville, Louisville, KT 40292, USA; [EMAIL_ADDRESS] (C.M.T. );  
[EMAIL_ADDRESS] (M.K.) 1  
[PERSON]  
1 These authors contributed equally to this work. 1  
[PERSON]  
1 Computer Science & Engineering Department, J.B. Speed School of Engineering, University of Louisville, Louisville, KT 40292, USA; [EMAIL_ADDRESS] (C.M.T. );  
[EMAIL_ADDRESS] (M.K.) 1  
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Deep neural networks consist of an input layer, output layer, and many hidden layers in the middle. Each layer consists of neurons which are inspired by how neurons in the brain process information. Each neuron has a weight that is updated using a technique called gradient descent during the training process. The weights of the network are optimized by minimizing a cost function. Nonlinearities are applied between layers to fit nonlinear functions from the input data to the output. Due to this, neural networks are able to model complex mappings. After training, the model is able to classify unseen data. Deep learning has achieved high performance for numerous types of cancers. The authors of [17] demonstrated accuracy up to 98.42 for breast cancer detection. In [18], they found that they could detect lung cancer with up to 97.67 using deep transfer learning. In the fall of 2018, researchers at Seoul National University Hospital and College of Medicine developed a machine learning algorithm called Deep Learning based Automatic Detection (DLAD) to analyze chest radiographs and detect abnormal cell growth, such as potential cancers. In [19], the algorithm's performance was compared to multiple physician's detection abilities on the same images and outperformed 17 of 18 doctors. These results demonstrate the potential for using deep learning to aid medical practitioners. Despite this performance, adoption of these systems in clinical environments has been stunted due to lack of explainability. Deep learning techniques are considered black boxes as depicted in Figure 2. Explainability attempts to mitigate the black box nature of these algorithms. Explainability is needed for a variety of reasons. First, there are legal and ethical requirements along with laws and regulations that are required for deep learning cancer detection systems to be implemented in a clinical setting. An example of a regulation is the European Union's General Data Protection Regulation (GDPR) requiring organizations that use patient data for classifications and recommendations to provide on-demand explanations [20]. The inability to provide such explanations on demand may result in large penalties for the organizations involved. There are also monetary incentives associated with explainable deep learning models. Beyond ethical and legal issues, clinicians and patients need to be able to trust the classifications provided by these systems [21]. Explanation is also needed for trust and transparency [22; 23; 24; 25; 26; 27; 28]. Explanation methods attempts to show the reasoning behind the model's classification therefore building a degree of trust between the system, clinician, and patient. This can reduce the number of misinformed results that would be a possible consequence of non-explainable systems. Finally, explainable deep learning systems will provide the clinician with additional usefulness such as localization and segmentation of the lesion location [29; 30; 31; 32].