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

Abstract

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.

CVD-HNet: Classifying Pneumonia and COVID-19 in Chest X-ray Images Using Deep Network

“…The concept of parameter sharing is also used by CNN. A feature map is created by applying a single filter to various portions of an input [ 56 ].…”

Role of Artificial Intelligence in COVID-19 Detection

“…Handcrafted feature extraction methods such as the discrete wavelet transform (DWT) [ 55 ] and gray-level co-occurrence matrix (GLCM), and Haralick texture features [ 56 ] are the more commonly used methods. In addition, the features are also extracted with the two-dimensional (2D) curvelet transform (CTf) [ 57 ], residual exemplar local binary pattern (ResExLBP) [ 58 ], first order statistical features (FOSF) [ 50 ], histogram of oriented gradients (HOG) [ 59 ], dual-tree complex contourlet transform (DTCT) [ 60 ], local directional number pattern (LDN) [ 61 ], Pillow library [ 62 ] and fractional multichannel exponent moments (FrMEMs) [ 63 ], local binary pattern (LBP) [ 64 ], and multichannel fractional order Legendre Fourier moments (MFrLFM) [ 65 ], to characterize textural information.…”

Human visualization system based intensive contrast improvement of the collected COVID-19 images

“…[1]. There are also some systems that use low-cost Raspberry Pi microcontrollers to control a system, detect, tracking, objects in image [2], [3] or even in medical field for diagnosis of infections like COVID-19 computerized tomography (CT) scans and chest X-ray images [4]. X-ray images that have been advanced in medical research are the main types of medical image.…”