COVID-19 diagnosis from CT scans and chest X-ray images using low-cost Raspberry Pi

The diagnosis of COVID-19 is of vital demand. Several studies have been conducted to decide whether the chest X-ray and computed tomography (CT) scans of patients indicate COVID-19. While these efforts resulted in successful classification systems, the design of a portable and cost-effective COVID-19 diagnosis system has not been addressed yet. The memory requirements of the current state-of-the-art COVID-19 diagnosis systems are not suitable for embedded systems due to the required large memory size of these systems (e.g., hundreds of megabytes). Thus, the current work is motivated to design a similar system with minimal memory requirements. In this paper, we propose a diagnosis system using a Raspberry Pi Linux embedded system. First, local features are extracted using local binary pattern (LBP) algorithm. Second, the global features are extracted from the chest X-ray or CT scans using multi-channel fractional-order Legendre-Fourier moments (MFrLFMs). Finally, the most significant features (local and global) are selected. The proposed system steps are integrated to fit the low computational and memory capacities of the embedded system. The proposed method has the smallest computational and memory resources,less than the state-of-the-art methods by two to three orders of magnitude, among existing state-of-the-art deep learning (DL)-based methods.

The authors are thankful to the reviewer for pointing out this issue. The abstract section is rewritten as suggested.

Comment 10:
The major contribution of the paper should be heighlited immediately after literature kind review.
Response 10: The authors are thankful to the reviewer for pointing out this issue. As suggested, the revised manuscript includes the major contributions; please refer to page 2, lines 43-57.

Comment:
Hope these above points will be more beneficial to the author for improvement of the paper.

Response:
The authors are thankful to the reviewer for pointing out this issue. All of the points were very helpful and helped the authors to enhance the paper quality.

Comment:
This paper design a Raspberry Pi Linux embedded system for COVID-19 diagnosis from CT Scans and Chest X-ray Images. The cost of this system is the smallest among the deep learningbased models. My major concerns are list below: Response: The authors are thankful for reviewer#2.

Comment 1:
In the introduction section, should discuss some recent state-of-the-art models so that the readers can know more about the new techniques in this field. There are some literature that may be useful to further improve the quality of this section.
[ref1]Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for covid-19, IEEE reviews in biomedical engineering, 2020; The authors are thankful to the reviewer for pointing out these remarkable articles. As suggested, we added a new section (Section 3) for the literature review where we discussed the suggested related articles on pages 6 and 7.

Comment 2:
As seen in Fig.3, the proposed system belongs to the multi-modality system, thus, the authors should discuss more these works.

Response 2:
The authors are thankful to the reviewer for pointing out this issue. We apologize for not clearly discuss this issue. The proposed system consists of two separate classifier models. Thus, there is no multi-modality in the proposed system. The user will input a chest X-ray image to the X-ray classifier or a CT scan image to the CT image classifier. Both models are designed the same way but separately. We have modified Fig. 3 to illustrate this idea. Besides, we mentioned that the two models are separate in the list of contributions in the Introduction Section, on page 2, lines 46-48 and on page 10, lines 283-284.

Comment:
This work presents a novel approach for rapid, on-device COVID-19 detection using Raspberry Pi. Despite the plethora of works on this topic, this one clearly stands-out due to the low computational requirements and the Raspberry Pi deployment. The experimental section is a bit short, but the results are convincing. I recommend acceptance, although a few things should be addressed:

Response:
The authors are thankful for reviewer#3.

Comment 1:
Is this the first scientific report of using Raspberry Pi to diagnose COVID from medical images? If yes, please state so and if not, please cite relevant work. I quickly searched but couldn't find anything very similar. I also recommend discussing the work by [2] since it also deals with ondevice inference.

Response 1:
The authors are thankful to the reviewer for pointing out this issue. Yes, the proposed work is the first work to use Raspberry Pi to diagnose COVID-19. As suggested, we stated that in both the Introduction and Conclusion sections. Besides, we discussed ref [2] in the revised manuscript on page 2, lines 43-48, and on page 12, lines 343, respectively. Besides, we discussed Ref [2] on page 6, lines 165-170.

Comment 2:
Can you please comment on the overall time and space complexity? The elaboration about complexity in MFrLFMs is great (although there, at least a comment on space complexity would also be useful). It would be great to see a similar elaboration on the LBP method? I think if the entire pipeline (Fig 3) can be expressed in terms of computational complexity in O-notation, this could be a major finding and contribution that might even be worth mentioning in the abstract. Also the time complexity could be briefly compared with a standard MLP/CNN to solidify how important this contribution is.

Response 2:
The authors are thankful to the reviewer for pointing out this issue. As suggested, the time and space complexities of the proposed work are discussed in detail a the end of Section 4.2, on pages 9-10, lines 238-260. Fig. 3 has two main compute-intensive tasks, which are local and global feature extraction. Thus, the time complexity and space complexity of Fig. 3 can be reduced to the sum of these two tasks.
Regarding the time complexity of the MLP/CNN, several factors control this process, including the number of layers, the number of neurons per layer. Besides, the model hyperparameters' value, such as the early stopping, can dramatically change the time complexity of the MLP/CNN model. Thus, it would be challenging to compute the existing MLP/CNN models' exact time complexity. For space complexity, Table 3 in the revised manuscript shows each model's space requirements, which reflects the space complexity of the proposed method and state-of-the-art methods.

Comment 3:
You performed a binary classification. Although it's questionable how practically relevant this is, it's beyond the scope of this work. But could you please briefly comment whether/how this could be extended to a multi-class classification.

Response 3:
The authors are thankful to the reviewer for pointing out this issue. In the revised manuscript, we discussed this issue in conclusion about future work, on page 16, lines 356-358.
Comment 4: Table 1: I understand that these results are based on three repeated runs. Could you please at least show the performance with one more digit precision and also indicate the standard deviation. Ideally, a cross validation should be performed to strengthen this finding.

Response 4:
The authors are thankful to the reviewer for pointing out this issue. As suggested, the required standard deviation is included in Table I. Besides, the authors already performed cross-validation. While this is not mentioned in the initial submission, it is discussed in the revised manuscript.