Classification Process Research Articles

A novel approach to detecting epileptic patients: complex network-based EEG classification

Abstract Detection of epileptic seizures is important for early diagnosis and treatment. It is known that the behavioral patterns of the brain in electroencephalogram (EEG) signals have huge and complex fluctuations. Diagnosing epilepsy by analyzing signals are costly process. Various methods are used to classify epileptic seizures. However, the inadequacy of these approaches in classifying signals makes it difficult to diagnose epilepsy. Complex network science produces effective solutions for analyzing interrelated structures. Using methods based on complex network analysis, it is possible to EEG signals analyze the relationship between signals and perform a classification process. In this study proposes a novel approach for classifying epileptic seizures by utilizing complex network science. In addition, unlike the studies in the literature, classification processes were carried out with lower dimensional signals by using 1-s EEG signals instead of 23.6-s full-size EEG signals. Using the topological properties of the EEG signal converted into a complex network, the classification process has been performed with the Jaccard Index method. The success of the classification process with the Jaccard Index was evaluated using Accuracy, F1 Score, Recall, and K-Fold metrics. In the results obtained, the signals of individuals with epileptic seizures were separated with an accuracy rate of 98.15%.

Evaluating novel in silico tools for accurate pathogenicity classification in epilepsy-associated genetic missense variants.

Determining the pathogenicity of missense variants in clinical genetic tests for individuals with epilepsy is crucial for guiding personalized treatment. However, achieving a definitive pathogenic classification remains challenging, with most missense variants still classified as variants of uncertain significance (VUS) and with the availability of many computational tools which may provide conflicting predictions. Here, we aim to evaluate the performance of state-of-the-art computational tools in pathogenicity prediction of missense variants in epilepsy-associated genes. This will assist in selecting the most appropriate tool and critically assess their use in clinical setting. We assessed the performance of nine in silico pathogenicity prediction tools for missense variants in epilepsy-associated genes on three carefully curated data sets. The first two data sets comprise missense variants in epilepsy associated genes that have been uploaded to ClinVar in the last year and were, therefore, not part of the training set of any of the nine considered tools. These two data sets are based on two different lists of epilepsy-associated genes and comprise ~700 and ~ 250 missense variants, respectively. The third data set includes ~400 missense variants within epilepsy-associated genes for which the functional effects have been determined experimentally and are therefore used here to infer pathogenicity. These three data sets represent the best available approximation to blind and independent test sets. Among the nine assessed tools, AlphaMissense (area under the curve [AUC]: .93, .88, and .95) and REVEL (AUC: .93, .88, and .93) showed the best classification performance, also outperforming other tools in the number of classified variants. We show which recently developed prediction tools achieve higher performance in epilepsy-associated genes and should be integrated, therefore, into the American College of Medical Genetics and Genomics/Association of Molecular Pathology (AGMC/AMP) variant classification process. Periodic reevaluation of genetic test results with newly developed or updated tools should be incorporated into standard clinical practice to improve diagnostic yield and better inform precision medicine.

Whole slide cervical cancer classification via graph attention networks and contrastive learning

Cervical cancer is one of the most common cancers among women, which seriously threatens women’s health. Early screening can reduce the incidence rate and mortality. Thinprep cytologic test (TCT) is one of the important means of cytological screening, which has high sensitivity and specificity, and has been widely used in the early screening of cervical cancer. The automatic diagnosis of whole slide images (WSIs) by computers can effectively improve the efficiency and accuracy of doctors’ diagnoses. However, current methods ignore the intrinsic relationships between cervical cells in WSIs and neglect contextual information from the surrounding suspicious areas, and therefore limit their robustness and generalizability. In this paper, we propose a novel two-stage method to implement the automatic diagnosis of WSIs, which constructs Graph Attention Networks (GAT) based on local and global fields respectively to capture their contextual information in a hierarchical manner. In the first stage, we extract representative patches from each WSI through suspicious cell detection, and then employ a Local GAT to classify cervical cells by capturing correlations between suspicious cells in image tiles. This classification process provides the confidence and feature vectors for each suspicious cell. In the second stage, we perform WSI classification using a Global GAT model. We construct graphs corresponding to top-Kg and bottom-Kg cells for each WSI based on results from Local GAT, and introduce a supervised contrastive learning strategy to enhance the discriminative power of the extracted features. Experimental results demonstrate that our proposed method outperforms conventional approaches and effectively showcases the benefits of supervised contrastive learning. Our source code and example data are available at https://github.com/feimanman/Whole-Slide-Cervical-Cancer-Classification.

Locust-based genetic classifier for abnormality identification in brain

This study investigates the loctus-based genetic classifier for abnormality identification in the brain. In medical care, clinical professionals have to spend a lot of time extracting, identifying, and segmenting the afflicted region from magnetic resonance brain images. Utilizing computer-aided approaches is crucial to overcome this restriction. Henceforth, this paper proposes an efficient classifier for diagnosing abnormalities in human brain using magnetic resonance images (MRI). The focus is on improving the accuracy and efficiency of medical image segmentation, specifically for brain tumours, to assist clinical professionals in early disease detection. In order to improve the image's quality through noise reduction, the Gaussian filter is employed during the pre-processing stage. The proposed tumour segmentation is based on the Otsu algorithm, and the gray-level co-occurrence matrix (GLCM) is used to extract the relevant features. In this work, the locust-based genetic classifier plays a crucial role in early brain disease identification and pinpointing the precise location of the damaged area. Accuracy, sensitivity, and specificity have been used to analyze and validate the outcomes of the proposed technique. The current study's findings indicate accurate prediction of brain abnormality and have a 98.9% accuracy rate, 97.2% specificity, and 96% sensitivity. The study presents a reliable and efficient method for diagnosing brain abnormalities using MRI. The combination of the proposed approaches significantly enhances the segmentation and classification processes, leading to high diagnostic accuracy. This approach offers practical benefits for clinical professionals by reducing the time and effort required for diagnosing brain abnormalities.

Decoupling Deep Learning for Enhanced Image Recognition Interpretability

The quest for enhancing the interpretability of neural networks has become a prominent focus in recent research endeavors. Prototype-based neural networks have emerged as a promising avenue for imbuing models with interpretability by gauging the similarity between image components and category prototypes to inform decision-making. However, these networks face challenges as they share similarity activations during both the inference and explanation processes, creating a tradeoff between accuracy and interpretability. To address this issue and ensure that a network achieves high accuracy and robust interpretability in the classification process, this article introduces a groundbreaking prototype-based neural network termed the “Decoupling Prototypical Network” (DProtoNet). This novel architecture comprises encoder, inference, and interpretation modules. In the encoder module, we introduce decoupling feature masks to facilitate the generation of feature vectors and prototypes, enhancing the generalization capabilities of the model. The inference module leverages these feature vectors and prototypes to make predictions based on similarity comparisons, thereby preserving an interpretable inference structure. Meanwhile, the interpretation module advances the field by presenting a novel approach: a “multiple dynamic masks decoder” that replaces conventional upsampling similarity activations. This decoder operates by perturbing images with mask vectors of varying sizes and learning saliency maps through consistent activation. This methodology offers a precise and innovative means of interpreting prototype-based networks. DProtoNet effectively separates the inference and explanation components within prototype-based networks. By eliminating the constraints imposed by shared similarity activations during the inference and explanation phases, our approach concurrently elevates accuracy and interpretability. Experimental evaluations on diverse public natural datasets, including CUB-200-2011, Stanford Cars, and medical datasets like RSNA and iChallenge-PM, corroborate the substantial enhancements achieved by our method compared to previous state-of-the-art approaches. Furthermore, ablation studies are conducted to provide additional evidence of the effectiveness of our proposed components.

Implementation of Support Vector Machine (SVM) for Classification of Heart Failure Patients (Case Study: Prabumulih City Hospital)

The heart is a vital organ that functions as a blood pump to meet the body's oxygen and nutrient needs. Disorders of the heart can result in disruptions to blood circulation, underscoring the importance of maintaining heart health to prevent heart disease. The optimal function of the heart depends on the condition of the cardiac muscles, valves, and proper pumping rhythm. Heart failure, as one of the heart diseases, refers to the heart's inability to meet the body's blood demands. The management of heart disease involves comprehensive medical services provided in hospitals. One effort to improve the quality of healthcare services is to enhance the performance of hospitals. The Regional General Hospital of Kota Prabumulih is one healthcare institution that addresses various diseases, including heart disease. However, the classification process for heart disease at the Regional General Hospital is still conducted manually, consuming time and inefficient. To address this issue, the development of an intelligent system capable of classifying heart failure based on patient symptoms is necessary. This research proposes the implementation of the Support Vector Machine (SVM) method as a classification model, which is a high-dimensional hypothesis learning system. It is hoped that this system can assist medical professionals in determining the initial steps in managing heart failure quickly and effectively.

Classification of Garlic Varieties with Fluorescent Spectroscopy Using Machine Learning

Machine learning techniques can produce fast, accurate and objective results in the analysis of agricultural products. These artificial intelligence-based systems are frequently encountered in studies on agriculture in the literature. This study reveals the usability of machine learning algorithms in classification of garlic cultivars using fluorescent spectroscopic data. For this, six types of garlic were used: Razgradski-11, Razgradski-12, Razgradski-115, Plovdivski-120, Yambolski-99 and Topolovgradski. In the first stage, the parsing analysis made from the fluorescent spectroscopic data of the garlics was carried out with seven different machine learning. The classification results of these seven types of machine learning algorithms were obtained. In the second stage, the classification results were obtained by adjusting the hyperparameters of each Machine Learning (ML) algorithm in order to control the improvability of the classification accuracy rates. Finally, performance metrics such as Specificity, precision, MCC, F1-Score of the classification processes obtained in the two stages were compared. In general, it was observed that the classification performances increased with the hyperparameter adjustment performed in the second stage. In this study, classification results with ML showed that fluorescent spectroscopy data of garlic strongly represented garlic species and provided high performance classification accuracy of 99.93% with Neural Network (NN), one of the machine learning methods using these data.

Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification

ABSTRACT Imbalanced class distributions pose a prevalent challenge in numerous classification problems, requiring effective strategies for learning from such skewed data. Traditional machine learning algorithms often struggle with imbalanced datasets, as they tend to bias their classification functions toward the majority class, resulting in suboptimal performance for minority classes. In our research, we propose a novel approach to address this challenge specifically tailored for Support Vector Machines (SVM), a well-established family of learning algorithms. Our method leverages a kernel trick to enhance the SVM’s classification capabilities on imbalanced datasets named KTI. It aims to streamline the classification process by incorporating adaptive data transformations within the algorithm itself, offering a more efficient and integrated solution for handling imbalanced data. Experimental evaluations conducted on diverse real-world datasets demonstrate the superior performance of our proposed strategy compared to existing methods, showcasing its potential for practical applications in classification tasks with skewed class distributions.

An Innovative Tool for Monitoring Mangrove Forest Dynamics in Cuba Using Remote Sensing and WebGIS Technologies: SIGMEM

The main objective of this work was to develop a viewer with web output, through which the changes experienced by the mangroves of the Gran Humedal del Norte de Ciego de Avila (GHNCA) can be evaluated from remote sensors, contributing to the understanding of the spatiotemporal variability of their vegetative dynamics. The achievement of this objective is supported by the use of open-source technologies such as MapStore, GeoServer and Django, as well as Google Earth Engine, which combine to offer a robust and technologically independent solution to the problem. In this context, it was decided to adopt an action model aimed at automating the workflow steps related to data preprocessing, downloading, and publishing. A visualizer with web output (Geospatial System for Monitoring Mangrove Ecosystems or SIGMEM) is developed for the first time, evaluating changes in an area of central Cuba from different vegetation indices. The evaluation of the machine learning classifiers Random Forest and Naive Bayes for the automated mapping of mangroves highlighted the ability of Random Forest to discriminate between areas occupied by mangroves and other coverages with an Overall Accuracy (OA) of 94.11%, surpassing the 89.85% of Naive Bayes. The estimated net change based on the year 2020 of the areas determined during the classification process showed a decrease of 5138.17 ha in the year 2023 and 2831.76 ha in the year 2022. This tool will be fundamental for researchers, decision makers, and students, contributing to new research proposals and sustainable management of mangroves in Cuba and the Caribbean.

A study on the classification performance of water-injection hydrocyclone

Aiming at the problem of fine particles entrainment in the underflow of hydrocyclone during the metal grinding classification process, a water-injection hydrocyclone was proposed to improve the classification efficiency. Through external injection of water on the wall surface of the cone section, the particles settling in the region were loosely graded so that the fine particles settled in the wall surface returned to the inner swirl again, thus reducing their entry into the underflow. Numerical simulation was used to explore the differences in the internal flow field characteristics and separation performance of the hydrocyclone after adding the water-injection structure. Then the industrial tests were conducted on the classification performance of the water-injection hydrocyclone. Numerical results showed that compared with the conventional hydrocyclone, the static pressure, tangential and axial velocity of the fluid inside the water-injection hydrocyclone increased, while the turbulence intensity decreased. The experimental results showed that with the increase of water-injection flow rate, the content of -74 μm particles in the underflow of water-injection hydrocyclone first decreased and then increased, and the comprehensive classification efficiency increased and then decreased accordingly. Compared with the conventional hydrocyclone, the content of -74 μm particles in the underflow of the water-injection hydrocyclone was reduced from 26.34% to 23.95%, and the comprehensive classification efficiency was increased from 69.22% to 73.03%, which mitigated the phenomenon of fine particles entrainment in the underflow.

ADVANCEMENTS IN ALZHEIMER’S DIAGNOSIS THROUGH MRI USING BAYESIAN CONVOLUTIONAL NEURAL NETWORKS AND VARIATIONAL INFERENCE

Alzheimer’s disease is one of the brain disorders that can be deadly in older. The disease is less treated and less recognized, but Alzheimer’s disease is now a significant public health problem. Early detection of the disease can significantly reduce symptoms. However, the lack of medical personnel makes handling this disease complex. Therefore, an automatic diagnosis of Alzheimer’s disease is needed with a Magnetic Resonance Imaging (MRI) examination to get an accurate diagnosis of the disease. This study classified the type of Alzheimer’s disease with deep learning methods using the Bayesian Convolutional Neural Network (BCNN) and the Variational Inference (VI) technique. It aims to determine image classification and accuracy level at the level of Alzheimer’s disease by using 2,400 brain MRI images, divided into three classes (non-demented, very mild demented, and mild demented) based on severity. The data was acquired from the kaggle.com website. We use a dataset scenario of 80% for training and 20% for testing, 100x100 pixels, kernel size 3x3, and optimizer Adam with epoch 200. The accuracy of the image classification process is 80%. The non-demented label predicts that the uncertainty is 0.371, and the other uncertainty prediction is 0.002. The ability to anticipate uncertainty enables clinicians to make informed decisions regarding the reliability of the model’s output and the need for additional validation or confirmation.

Enhanced Neonatal Brain Tissue Analysis via Minimum Spanning Tree Segmentation and the Brier Score Coupled Classifier

Automatic assessment of brain regions in an MR image has emerged as a pivotal tool in advancing diagnosis and continual monitoring of neurological disorders through different phases of life. Nevertheless, current solutions often exhibit specificity to particular age groups, thereby constraining their utility in observing brain development from infancy to late adulthood. In our research, we introduce a novel approach for segmenting and classifying neonatal brain images. Our methodology capitalizes on minimum spanning tree (MST) segmentation employing the Manhattan distance, complemented by a shrunken centroid classifier empowered by the Brier score. This fusion enhances the accuracy of tissue classification, effectively addressing the complexities inherent in age-specific segmentation. Moreover, we propose a novel threshold estimation method utilizing the Brier score, further refining the classification process. The proposed approach yields a competitive Dice similarity index of 0.88 and a Jaccard index of 0.95. This approach marks a significant step toward neonatal brain tissue segmentation, showcasing the efficacy of our proposed methodology in comparison to the latest cutting-edge methods.

Hybrid Modeling and Simulation of the Grinding and Classification Process Driven by Multi-Source Compensation

Hybrid Modeling and Simulation of the Grinding and Classification Process Driven by Multi-Source Compensation

Implementation of Machine Learning in the Categorization of Improvement Areas for Kaizen Submissions

This paper explores the synergy between Kaizen methodologies and Machine Learning, with the goal of improving the efficiency of categorizing areas of improvement. The implementation of Machine Learning algorithms, utilizing the ML.NET tool, has proven to be highly advantageous. These algorithms automate the categorization of Kaizen proposals based on structured data and predefined labels. This automation significantly reduces manual workload, expedites the classification process, and ultimately accelerates decision-making within the realm of continuous improvement. Furthermore, the application of machine learning has improved the categorization accuracy, reduced the risk of human errors, and ensured a more precise assignment to the relevant categories. This, in turn, enhances the quality of the collected information, facilitating well-informed decision-making. Additionally, the automated categorization has streamlined the process for users submitting Kaizen proposals, eliminating the need for manual category selection. This reduces cognitive load and promotes active engagement in the continuous improvement process. Furthermore, the system has introduced a category-based incentive structure, where users accrue points corresponding to the category assigned to their Kaizen proposals. These points can be exchanged for items provided by the company, acting as an additional incentive for active employee participation in the continuous improvement process. In conclusion, the integration of Machine Learning with the ML.NET tool has become a valuable tool for optimizing the management of improvement proposals, resulting in a marked enhancement in efficiency, accuracy, and user engagement in the categorization process.

Preprocessing Text Data for the Doc2Vec Model in the Automatic Classification of Tickets in Support Centers of Software Providers

Leading software providers typically implement customer technical support functions, which are crucial for promoting and enhancing the competitiveness of their products and services in global markets. The high volume and heterogeneity of support tickets (functional, temporal, linguistic, etc.) highlight the importance of efficient classification systems. Effective classification optimizes the distribution of these tickets among support center specialists and automates their processing using an established knowledge base. However, classifying these tickets is a loosely formalized task. For companies that have accumulated substantial data on customer requests, automating classification through machine learning methods and natural language processing models, such as Word2Vec, FastText, BERT, and GPT, becomes feasible. It is generally accepted that classification effectiveness primarily depends on the model employed. Nevertheless, the quality of these models is significantly influenced by the nature of the training data. Literature review of the reveals significant research interest in methods for the automatic classification of tickets specifically tailored to the operational conditions of software provider support centers. However, there is a noticeable gap in the literature regarding the impact of data preprocessing on the quality of these models. The article aims to clarify the techniques of data preprocessing and analyze their impact on the effectiveness of text classification, considering the specificity of software provider support centers. This study examines the stages of the automatic classification process for tickets, accounting for the unique characteristics of the data (customer text requests). A relevant set of specified methodological and instrumental tools was developed and tested using open data from a global software provider (DevExpress). The testing involved a database of 165,000 tickets. The study's results indicate that preprocessing can improve classification metrics such as F-measure, Precision, and Recall from 77% to 79%. Additionally, preprocessing significantly reduces the dimensionality of text data (by 48.2%) and increases model training speed (by 26.5%) without loss of accuracy, ensuring cost-efficiency and operational efficiency in the use of computational resources.

A hybrid detection model for acute lymphocytic leukemia using support vector machine and particle swarm optimization (SVM-PSO).

Leukemia, a hematological disease affecting the bone marrow and white blood cells (WBCs), ranks among the top ten causes of mortality worldwide. Delays in decision-making often hinder the timely application of suitable medical treatments. Acute lymphoblastic leukemia (ALL) is one of the primary forms, constituting approximately 25% of childhood cancer cases. However, automated ALL diagnosis is challenging. Recently, machine learning (ML) has emerged as an important tool for building detection models. In this study, we present a hybrid detection model that improves the accuracy of the detection process by combining support vector machine (SVM) and particle swarm optimization (PSO) approaches to automatically identify ALL. We use SVM to represent a two-dimensional image and complete the classification process. PSO is employed to enhance the performance of the SVM model, reducing error rates and enhancing result accuracy. The input images are obtained from two public datasets (ALL-IDB1 and ALL-IDB2), and public online datasets are utilized for training and testing the proposed model. The results indicate that our hybrid SVM-PSO model has high accuracy, outperforming stand-alone algorithms and demonstrating superior performance, an enhanced confusion matrix, and a higher detection rate. This advancement holds promise for enhancing the quality of technical software in the medical field using machine learning.

Sarcasm Detection Based on Sentiment Analysis of Audio Corpus Using Deep Learning

With the rapid progress in digitalization, hundreds of services and applications have begun to depend on human-machine communication, primarily voice-based. As technology advances, it has become increasingly imperative to develop systems that can discern sophisticated emotions from humans, like sarcasm. Despite research that has been conducted on the detection and analysis of speech signals to try and understand emotion, the detection of sarcasm, which is often not overtly expressed, is quite far from something that has received the attention it deserves in recent years. It plays a very important role in any utterance, searching to identify the psychology, mood, or even health behind it. Hence, it does give interactions with humans a more human-like flavor while raising the feeling of understanding human emotions. This article tries to identify sarcasm in speech using audio data derived from real-world, spontaneous, monolingual corpora. In this classification process, a deep learning classifier known as CNN-LSTM model is used. Results of the study showed that a robust combination between feature extraction through convolutional layers and sequential learning through LSTM layers can identify intricate speech patterns in sarcasm. Experimented and validated in terms of performance, this model shows the model to be very good at discriminating between sarcastic and no sarcastic speech, thus promoting it for more extensive use in various applications related to sentiment analysis and in human-computer interaction applications. The results show that this system provides far much closer approximations to human interaction styles, but its impact would extend into a wide area from customer services, monitoring mental health, and even AI-driven communication systems. It is a monolingual and spontaneous corpus that will open the gates for further work and development down the line and possibly extend to multilingual and more diversified speech contexts, thus fine-tuning accuracy in practical scenarios of sarcasm detection.

Analysis of the Severity of Heterogeneity Protection Forest based on SVM and PCA

Forest heterogeneity indicates the forest condition on producing more carbon into environment. Semidang Bukit Kabu Hunting Park Forest is a nature reserve lies over two districts of Central Bengkulu and Seluma, Bengkulu Province, which should have a heterogeneous forest to protect its natural resources. However, the data showed that the condition of it does not appear to have dense forest heterogeneity anymore, and its rate still remain unknown. Remote sensing as one of tools to help the remote monitoring was believed to be helpful to this question. This study showed changes in the heterogeneity from 2016 to 2021. Sentinel-2 imageries were occupied to help the process of classification of forest and non-forest areas. Support Vector Machine, as one of powerful machine learning tools, was also help the process with the integrating of Principal Component Analysis to optimize forest characteristics. This study indicates that there are significant reductions of forest heterogeneity over the area. The number of forest (heterogeny areas) continues to decline from 8122 ha in 2016 to 4339 ha in 2021. Furthermore, this study had proven that the algorithm of support vector machines showed significant performance to build the model towards the data with overall accuracy rate of 0.9434 and a kappa index of 0.9833.

Cramer Distance: A Deep Learning Approach for Better Epileptic Seizure Prediction

Epilepsy is a neurological condition that is found in most people all over the world, and the ability to accurately anticipate seizures in epileptic patients has a significant impact on both their level of protection and their overall quality of life. This research proposes a unique patient specific seizure prediction approach based on Deep Learning (DL) using long-term scalp electroencephalogram (EEG) recordings to predict seizure onset. Preictal brain states should be adequately detected and differentiated from the prevalent interictal brain states as early as possible to make this technology acceptable for real-time use. A single automated system has been designed for the Features Extraction (FE) and classification processes. The raw EEG signal that has not been pre-processed is considered the input to the system, and the signal is further reduced using subsequent computations. An innovative reconstruction approach using Variational Auto-Encoder Generative Adversarial Networks (VAE+C+GAN) with the Cramer Distance (CD) and a Temporal-Spatial-Frequency (TSF) loss function is presented in this research work. The machine that discriminates receives instructions to differentiate between created tests and actual samples, while the generator is verified to produce false samples that the discriminator does not recognize as fake. The proposed VAE+C+GAN’s experimental results have been examined, and a classification accuracy of 95% has been achieved. According to the experiment's findings, the VAE-C-GAN performs better than the current EEG classification system and has excellent potential for real-time applications.

Matrix-assisted laser desorption/ionization mass spectrometry imaging as a new tool for molecular histopathology in epilepsy surgery.

Epilepsy surgery is a treatment option for patients with seizures that do not respond to pharmacotherapy. The histopathological characterization of the resected tissue has an important prognostic value to define postoperative seizure outcome in these patients. However, the diagnostic classification process based on microscopic assessment remains challenging, particularly in the case of focal cortical dysplasia (FCD). Imaging mass spectrometry is a spatial omics technique that could improve tissue phenotyping and patient stratification by investigating hundreds of biomolecules within a single tissue sample, without the need for target-specific reagents. An insitu proteomic technique called matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is here investigated as a potential new tool to expand conventional diagnosis on standard paraffin brain tissue sections. Unsupervised and region of interest-based MALDI-MSI analyses of sections from 10 FCD type IIb (FCDIIb) cases were performed, and the results were validated by immunohistochemistry. MALDI-MSI identified distinct histopathological features and the boundaries of the dysplastic lesion. The capability to visualize the spatial distribution of well-known diagnostic markers enabling multiplex measurements on single tissue sections was demonstrated. Finally, a fingerprint list of potential discriminant peptides that distinguish FCD core from peri-FCD tissue was generated. This is the first study that explores the potential application of MALDI-MSI in epilepsy postsurgery fixed tissue, by utilizing the well-characterized FCDIIb features as a model. Extending these preliminary analyses to a larger cohort of patients will generate spectral libraries of molecular signatures that discriminate tissue features and will contribute to patient phenotyping.