Super-Resolution of DermaMNIST Images Using Deep Learning Models

The use of artificial intelligence and deep neural networks is becoming increasingly important in the field of medical diagnostics. Medical images often have a problem with low resolution and unclear details, which, if misinterpreted, can lead to errors in diagnosis. Super-resolution algorithms can assist not only in rescaling the images, but also in enhancing the quality of the image itself, and better visibility of fine details, textures, and contours in the images. In this paper, a comparison of three super-resolution models was made: Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR), Efficient Sub-Pixel CNN (ESPCN), and Super-Resolution Convolutional Neural Network (SRCNN). The comparison of these three models was made to improve the quality and scaling of dermatoscopic images. The DermaMNIST data set was used and 3 scalings cases were considered: from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{3 2 \times 3 2}$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{6 4 \times 6 4, ~} \mathbf{6 4 \times 6 4}$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$128 \times 128$</tex>, and from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$32 \times 32$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$128 \times 128$</tex>. The results confirm that all tested models achieve significant performance, confirming the state-of-the-art results with all of them exceeding the value of the PSNR metric of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{3 0 ~ d B}$</tex> and the SSIM metric of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{7 5 \%}$</tex>. It is particularly noteworthy that two models, in the case of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2 \times$</tex> scaling, achieved PSNR values above 40 dB and SSIM above <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 5 \%}$</tex>, which further confirmed their potential for application in medical image processing.