# Smart Classification Paradigms for Protein Samples from 1-D Electrophoresis Gel +
## 2 State of Art in Electrophoresis and Image Retrieval
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Paradigm** & **Sample** & **Description** & **Author** \\ \hline Mass spectrography & 2D gel & Identify complete & ([PERSON], 2018) [3] \\  & electrophoresis & protein spots sequences & \\  & & with analysis and processing & \\  & & of images, does not make & \\  & & any recognition, does not & \\  & & measure molecular weights & \\  & & they does not make any & \\  & & recognition or retrieval process. & \\ \hline Total Lab & 1D gel & A comparison is made & ([PERSON], 2012) [4] \\ software & electrophoresis & between both software. & \\ and & & The determines that & \\ Lab Image & & electrophoresis data or & \\ software & & measure molecular weights & \\  & & can vary depending on & \\  & & software, they does not make & \\  & & any recognition or retrieval process. & \\ \hline Image analysis & 2D gel & A 95\% of confidence is & ([PERSON], 2018) [5] \\ Wavelet Transform & electrophoresis & obtained in electrophoresis & \\ DB8-15 & & images of protein detection & \\ BIOR 4.4-7 & & applied at fibromyalgia & \\ BIOR 3.3-4 & & patients & \\ \hline Image analysis, and & 2D gel & Analysis of capillary samples & ([PERSON], 2019) [6] \\ mass spectrography and measures & electrophoresis & cell or molecular analysis & \\ molecular weight & & & \\ \hline Image analysis, with & 1D gel & Enhancement of & ([PERSON], 2017) [7] \\ Preprocessing and & electrophoresis & electrophoresis spots & \\ contrast & & only focus on the detection & \\  & & or processing of protein stains & \\ \hline Image analysis with & Content based & Make a recovery of similar & ([PERSON], 2018) [8] \\ location histogram & system & images using location & \\  & & histogram & \\ \hline Image analysis & Myocardium & Using image samples of & ([PERSON], 2019) [9] \\ with artificial neural network & images & patients with heart diseases & \\ \hline Decision tree & Back pains & Data analysis with 10 variables & ([PERSON], 2018) [10] \\  & diagnosis in & and decision trees in numerical data with 78.39\% & \\ \hline Artificial neural & Numerical data & Statistical analysis of iliac fossa & ([PERSON], 2019) [11] \\ networks, decision trees (CHAID) & & for diagnostic with 74.2\% of & \\  & & accuracy in decision trees and & \\  & & 75\% in artificial neural network & \\ \hline Artificial neural & Numerical data & Diagnosis of acute coronary & ([PERSON], 2017) [12] \\ networks and & & syndrome with 96\% of & \\ genetic algorithms & & accuracy with 7 characteristics & \\ \hline Adaptive fuzzy neural & Data prediction & Data prediction making a & ([PERSON], 2019) [13] \\ network and genetic & & comparative analysis in & \\ algorithm & & multi objective optimization & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative table of related works in electrophoresis and/or diverse paradigmsMore specifically, from the three strongest related works we can say specifically that:  
1. In [PERSON]'s 2012 paper [4], a molecular weight measurement is performed, but it is highlighted that there are calculation irregularities depending on commercial versions of the compared software. 2. In the case of [PERSON] and [PERSON] of 2018 [5], a molecular weight detection with 95% accuracy is performed using also wavelet transform but with different families or levels. 3. In the work of [PERSON] of 2019 [6], a cell detection in electrophoresis is done by means of digital image processing, without making a sample recovery or a molecular weight measure. Our main contribution lies in providing image analysis with wavelet transforms, the novel contribution lies in a visual retrieval system of query images and a 96.7% in molecular weight detection unlike the 95% of [PERSON] and [PERSON]. Our proposal is a free system unlike the commercial Total Lab and Lab Image software analyzed by [PERSON] [4]. Even though other work has been done with wavelet retrieval, the percentage is surpassed, and the content-based retrieval system in electrophoresis images is not provided in other works so far.