Cancer Classification System Research Articles

Quantum-Classical Hybrid Cancer Classification System

Quantum-Classical Hybrid Cancer Classification System

Automated CAD system for early detection and classification of pancreatic cancer using deep learning model

Accurate diagnosis of pancreatic cancer using CT scan images is critical for early detection and treatment, potentially saving numerous lives globally. Manual identification of pancreatic tumors by radiologists is challenging and time-consuming due to the complex nature of CT scan images and variations in tumor shape, size, and location of the pancreatic tumor also make it challenging to detect and classify different types of tumors. Thus, to address this challenge we proposed a four-stage framework of computer-aided diagnosis systems. In the preprocessing stage, the input image resizes into 227 × 227 dimensions then converts the RGB image into a grayscale image, and enhances the image by removing noise without blurring edges by applying anisotropic diffusion filtering. In the segmentation stage, the preprocessed grayscale image a binary image is created based on a threshold, highlighting the edges by Sobel filtering, and watershed segmentation to segment the tumor region and we also implement the U-Net method for segmentation. Then refine the geometric structure of the image using morphological operation and extracting the texture features from the image using a gray-level co-occurrence matrix computed by analyzing the spatial relationship of pixel intensities in the refined image, counting the occurrences of pixel pairs with specific intensity values and spatial relationships. The detection stage analyzes the tumor region’s extracted features characteristics by labeling the connected components and selecting the region with the highest density to locate the tumor area, achieving a good accuracy of 99.64%. In the classification stage, the system classifies the detected tumor into the normal, pancreatic tumor, then into benign, pre-malignant, or malignant using a proposed reduced 11-layer AlexNet model. The classification stage attained an accuracy level of 98.72%, an AUC of 0.9979, and an overall system average processing time of 1.51 seconds, demonstrating the capability of the system to effectively and efficiently identify and classify pancreatic cancers.

Effective Skin Cancer Diagnosis Through Federated Learning and Deep Convolutional Neural Networks

ABSTRACT Skin cancer is a prevalent type of cancer that affects millions of people globally. However, detecting it can be a challenging task, even for specialized dermatologists. Early detection is crucial for successful treatment, and deep learning techniques, particularly deep convolutional neural networks (DCNNs), have shown tremendous potential in this area. However, achieving high accuracy results requires large volumes of data for training these DCNNs. Since medical organizations and institutions, individually, do not usually have such amounts of information available, and due to the current regulations regarding intellectual property and privacy of medical patient data, it is difficult to share data in a direct way. The primary objective of this work is to overcome this issue through a federated learning approach. We created a privacy-preserving and accurate skin cancer classification system that can assist dermatologists and specialists in making informed patient care decisions. The federated learning DCNNs architecture uses a combination of convolutional and pooling layers to extract relevant features from skin lesion images. It also includes a fully connected layer for classification. To evaluate the proposed architecture, we tested it on three datasets of varying complexity and size. The results demonstrate the applicability of the proposed solution and its efficiency for skin cancer classification.

An optimized support vector machine for lung cancer classification system

IntroductionLung cancer is one of the main causes of the rising death rate among the expanding population. For patients with lung cancer to have a higher chance of survival and fewer deaths, early categorization is essential. The goal of thisresearch is to enhance machine learning to increase the precision and quality of lung cancer classification.MethodsThe dataset was obtained from an open-source database and was utilized for testing and training. The suggested system used a CT scan picture as its input image, and it underwent a variety of image processing operations, including segmentation, contrast enhancement, and feature extraction.ResultsThe training process produces a chameleon swarm-based supportvector machine that can identify between benign, malignant, and normal nodules.ConclusionThe performance of the system is evaluated in terms of false-positive rate (FPR), sensitivity, specificity, recognition time and recognition accuracy.

Decoding skin cancer classification: perspectives, insights, and advances through researchers’ lens

Skin cancer is a significant global health concern, with timely and accurate diagnosis playing a critical role in improving patient outcomes. In recent years, computer-aided diagnosis systems have emerged as powerful tools for automated skin cancer classification, revolutionizing the field of dermatology. This survey analyzes 107 research papers published over the last 18 years, providing a thorough evaluation of advancements in classification techniques, with a focus on the growing integration of computer vision and artificial intelligence (AI) in enhancing diagnostic accuracy and reliability. The paper begins by presenting an overview of the fundamental concepts of skin cancer, addressing underlying challenges in accurate classification, and highlighting the limitations of traditional diagnostic methods. Extensive examination is devoted to a range of datasets, including the HAM10000 and the ISIC archive, among others, commonly employed by researchers. The exploration then delves into machine learning techniques coupled with handcrafted features, emphasizing their inherent limitations. Subsequent sections provide a comprehensive investigation into deep learning-based approaches, encompassing convolutional neural networks, transfer learning, attention mechanisms, ensemble techniques, generative adversarial networks, vision transformers, and segmentation-guided classification strategies, detailing various architectures, tailored for skin lesion analysis. The survey also sheds light on the various hybrid and multimodal techniques employed for classification. By critically analyzing each approach and highlighting its limitations, this survey provides researchers with valuable insights into the latest advancements, trends, and gaps in skin cancer classification. Moreover, it offers clinicians practical knowledge on the integration of AI tools to enhance diagnostic decision-making processes. This comprehensive analysis aims to bridge the gap between research and clinical practice, serving as a guide for the AI community to further advance the state-of-the-art in skin cancer classification systems.

Comparison Between AJCC 8th Edition Gastric and Esophageal ypTNM Classification System in Siewert II Esophagogastric Junction Adenocarcinomas: A Retrospective Study Based on Eastern and Western Data

Multimodal therapy has become the standard treatment for curable upper gastrointestinal cancers. However, it remains unclear which 8th edition AJCC post-neoadjuvant therapy pathological classification system, esophageal (ypTNM-EC) or gastric (ypTNM-GC), can predict the overall survival (OS) of patients with Siewert II adenocarcinomas better. Patients diagnosed with Siewert II adenocarcinomas receiving neoadjuvant therapy plus curative resection at the University Medical Center Utrecht (UMC Utrecht) and the Peking University Cancer Hospital (PUCH) between the 2001 and 2022 were included in this study. The patients from two institutions were analyzed separately. Predictive univariable and multivariable Cox models based on ypTNM-EC and ypTNM-GC were constructed. The C-index and calibration curves were used to compare the predictive abilities of ypTNM-EC and ypTNM-GC Cox models. A total of 125 patients from UMCU and 145 from PUCH were included. There was no significant difference in the C-index between the ypTNM-EC and the ypTNM-GC univariable and multivariable Cox models in the UMC Utrecht (p-value=0.883; p-value=0.681) and PUCH (p-value=0.808; p-value=0.548) cohorts, and no significant difference was observed between their calibration curves in the two cohorts. The AJCC 8th edition ypTNM prognostic classification systems for esophageal and gastric cancer demonstrated no difference in predicting OS for patients with Siewert II adenocarcinomas both in the Western and Eastern data. The ypTNM-GC, with fewer stage groups, may offer greater convenience for clinical application.

Automatic cervical cancer classification using adaptive vision transformer encoder with CNN for medical application

Accurate and early cervical cancer screening can reduce the mortality rate of cervical cancer patients. The Pap test, often known as a Pap smear, is one of the frequently used methods for the early diagnosis of cervical cancer. However, manual analysis can be time-consuming. Previous approaches have faced challenges such as low accuracy, increased computing complexity, larger feature dimensionality, poor reliability, and increased time consumption due to subpar hyper-parameter optimization. This paper proposes an automatic cervical cancer classification system using a deep learning algorithm to address these issues. The proposed system consists of three stages: pre-processing, segmentation, and classification. Initially, images are collected and pre-processed through normalization, smoothing, and resizing. The pre-processed images are then passed to the segmentation stage, where an Adaptive Deep Residual Aggregation Network is utilized (ADRAN). After segmentation, the images are classified into seven categories: Carcinoma_in_situ, Light_dysplastic, Moderate_dysplastic, Normal_columnar, Normal_Intermediate, Normal_superficial, and Severe_dysplastic using an Adaptive Vision Transformer Encoder (AVTE) with CNN. To improve the efficiency of the transformer learning network, the hyperparameters of AVTE with CNN are optimized using an Adaptive Cat Swarm Optimization algorithm (ACSO). The efficiency of the presented technique is evaluated based on various metrics, and experimentation is conducted using the Herlev dataset.

Copy number variation heterogeneity reveals biological inconsistency in hierarchical cancer classifications

Cancers are heterogeneous diseases with unifying features of abnormal and consuming cell growth, where the deregulation of normal cellular functions is initiated by the accumulation of genomic mutations in cells of - potentially - any organ. At diagnosis malignancies typically present with patterns of somatic genome variants on diverse levels of heterogeneity. Among the different types of genomic alterations, copy number variants (CNV) represent a distinct, near-ubiquitous class of structural variants. Cancer classifications are foundational for patient care and oncology research. Terminologies such as the National Cancer Institute Thesaurus provide large sets of hierarchical cancer classification vocabularies and promote data interoperability and ontology-driven computational analysis. To find out how categorical classifications correspond to genomic observations, we conducted a meta-analysis of inter-sample genomic heterogeneity for classification hierarchies on CNV profiles from 97,142 individual samples across 512 cancer entities, and evaluated recurring CNV signatures across diagnostic subsets. Our results highlight specific biological mechanisms across cancer entities with the potential for improvement of patient stratification and future enhancement of cancer classification systems and provide some indications for cooperative genomic events across distinct clinical entities.

MALEXNET-EUNET: MULTI-CLASS LIVER CANCER CLASSIFICATION AND SEGMENTATION USING A HYBRID DEEP LEARNING SYSTEM

Background and objective: One of the most prevalent and significant causes of cancer-related mortality worldwide is considered to be liver cancer. Techniques for automatically segmenting and classifying liver tumors are crucial for supporting medical professionals during the tumor diagnostic process. Classifying liver tumors is challenging due to noise, nonhomogeneity, and the significant appearance diversity observed in tumor tissue. Also, in recent years, most of the research has performed binary classification, but there is still a lack of research on multi-class liver cancer classification. Therefore, we perform a multi-class liver cancer classification and segmentation in this research. Methods: We propose a hybrid deep learning-based multi-class liver cancer classification and segmentation system in this research. The collected CT images are pre-processed in four stages: contrast enhancement, noise filtering, smoothing and sharpening, and liver region segmentation. Next, the binary, texture, histogram, and rotational, scalability, and translational (RST) features are extracted from the pre-processed images. Then, the average correlation (AC) and probability of error (POE) approaches are applied to selecting relevant features and excluding less significant features. After feature selection, a modified AlexNet (MAlexNet) model is used to classify the multi-class classification of liver tumors. Finally, the identified liver tumor regions are segmented using the enhanced U-Net (EUNet) model. Results: Identified liver tumors are accurately classified into three different categories using the proposed classification model: hemangioma (HEM), hepatocellular carcinoma (HCC), and metastatic carcinoma (MET), with an average accuracy of 99.19%. We have collected the multi-class liver cancer CT images from real time patients. The results of the experiments show that proposed hybrid system provides adequate overall accuracy, is less noise-sensitive, and outperforms other state-of-the-art techniques such as SVM, Faster RCNN, Mask RCNN, and SAR-U-Net on a wide range of CT images. Conclusion: Radiologists and doctors can identify liver tumors more accurately using the suggested innovative framework.

Chicken swarm algorithm with deep convolutional neural network based tongue image analysis for gastric cancer classification

Tongue images (the size, shape, and colour of tongue and the thickness, colour, and moisture content of tongue coating), reflecting the medical condition of entire body based on the model of traditional Chinese medicine (TCM) are extremely utilized in China for millions of years. Gastric cancer (GC) is great lethal kind of cancer in countries and societies. The screening and analysis of GC yet depend on gastroscopy, however its application was significantly restricted due to its invasive, maximum rate and the requirement for expert endoscopists. Early recognition in GC patients and direct treatment contribute significantly to safety for health. Consequently, this study introduces a Chicken Swarm Algorithm with Deep learningbased Tongue Image Analysis for Gastric Cancer Classification (CSADL-TIAGCC) system. The projected CSADL-TIAGCC approach studies the input tongue images for the identification and classification of GC. To accomplish this, the CSADL-TIAGCC system uses improved U-Net segmentation approach. Besides, residual network (ResNet-34) model-based feature extractor is used. Furthermore, long short term memory (LSTM) approach was exploited for GC classification and its hyperparameters are selected by the CSA. The simulation outcome of the CSADL-TIAGCC algorithm was examined under tongue image database. The experimental outcomes illustrate the enhanced results of the CSADL-TIAGCC technique with respect of different evaluation measures.

TF LITE BASED BLOOD CANCER CLASSIFICATION SYSTEM

Blood cancer, comprising various hematologic malignancies such as leukemia, lymphoma, and myeloma, presents significant diagnostic challenges that require precise and timely identification for effective treatment. The TF-Based Blood Cancer Classification System leverages the capabilities of TensorFlow (TF) to develop a robust, scalable, and accurate machine learning model for classifying different types of blood cancer from medical imaging and genetic data. This system integrates advanced deep learning techniques, including convolutional neural networks (CNNs) for image analysis and recurrent neural networks (RNNs) for genetic sequence analysis, to process and interpret complex biomedical data. The proposed system employs a comprehensive dataset containing labeled images and genetic profiles of various blood cancer types. Data preprocessing steps, such as normalization and augmentation, are applied to enhance model performance and generalization. The classification model is trained and validated using TensorFlow’s high- performance computing capabilities, achieving high accuracy and reliability in distinguishing between different blood cancer subtypes. Initial results demonstrate the system's potential to significantly improve diagnostic accuracy and speed, outperforming traditional diagnostic methods. This advancement not only aids healthcare professionals in making informed decisions but also contributes to personalized treatment strategies. The TF-Based Blood Cancer Classification System represents a significant step towards integrating artificial intelligence in medical diagnostics, offering promising prospects for improving patient outcomes in hematologic oncology. Keywords: Blood cancer, TensorFlow, machine learning, deep learning, convolutional neural networks, recurrent neural networks, medical imaging, genetic data, diagnostics, hematologic malignancies.

DS23 Evaluation of American Joint Committee on Cancer and alternative tumour classification systems for primary penile squamous cell carcinoma

DS23 Evaluation of American Joint Committee on Cancer and alternative tumour classification systems for primary penile squamous cell carcinoma

Overexpression of TSPAN8 in consensus molecular subtype 3 colorectal cancer

BackgroundRecently, consensus molecular subtypes (CMSs) have been proposed as a robust transcriptome-based classification system for colorectal cancer (CRC). Tetraspanins (TSPANs) are transmembrane proteins. They have been associated with the development of numerous malignancies, including CRC, through their role as “master organizers” for multi-molecular membrane complexes. No previous study has investigated the correlation between TSPANs and CMS classification. Herein, we investigated the expression of TSPANs in patient-derived primary CRC tissues and their CMS classifications. MethodsRNA samples were derived from primary CRC tissues (n = 100 patients diagnosed with colorectal adenocarcinoma) and subjected to RNA sequencing for transcriptome-based CMS classification and TSPAN-relevant analyses. Immunohistochemistry (IHC) and immunofluorescence (IF) stains were conducted to observe the protein expression level. To evaluate the relative biological pathways, gene-set enrichment analysis was performed. ResultsOf the highly expressed TSPAN genes in CRC tissues (TSPAN8, TSPAN29, and TSPAN30), TSPAN8 was notably overexpressed in CMS3-classified primary tissues. The overexpression of TSPAN8 protein in CMS3 CRC was also observed by IHC and IF staining. As a result of gene-set enrichment analysis, TSPAN8 may potentially play a role in organizing signaling complexes for kinase-based metabolic deregulation in CMS3 CRC. ConclusionsThe present study reports the overexpression of TSPAN8 in CMS3 CRC. This study proposes TSPAN8 as a subtype-specific biomarker for CMS3 CRC. This finding provides a foundation for future CMS-based studies of CRC, a complex disease and the second leading cause of cancer mortality worldwide.

Pediatric cancer registry in Turkey 2002-2023: 22 years of achievement (TPOG & TPHD).

10051 Background: The survival rates for childhood cancer have seen significant improvements in high-income countries over the past few decades. However, in low- and middle-income countries, it remains a substantial challenge. Access to care remains a significant barrier to improving survival rates in low- and middle-income countries (LMICs). The pediatric cancer registry is a crucial component of pediatric cancer control efforts. In this context, we present the findings from 22 years of the pediatric cancer registry in Turkey. Methods: The present pediatric cancer registry was established by Turkish Pediatric Oncology Group and Turkish Pediatric Hematology Association in 2002. The childhood cancer cases registered between 2002-2023 was included in this analysis. International Childhood Cancer Classification System was used for the classification. Essential demographic findings, ICD-O-3 morphology and topography codes were recorded for each case. Results: During the 22 years from 2002 to 2023, 50887 cases were registered. For all cases, median age was 6.5 year (0-19; M/F 28641/22233, 6 hermaphrodite, 7 unknown). Age distribution was 0-4 yrs, 41.1%; 5-9 yrs, 24.7%; 10-14 yrs, 23.1%; 15-19 yrs, 11.0%) The distribution of the tumor types were [number of cases, percentage of total, median age yrs, M/F]: Leukemia (14633, 28.8%, 5.6, 8510/6123); Lymphoma & other RES tumors (8958, 17.6%, 9.2, 5956/3000, 2 unknown); CNS [brain & spinal] (7299, 14.3%, 7.0, 4066/3232, 1 unkown); Sympathetic system (3783, 7.4%, 2.4, 1951/1831, 1 hermaphrodite); Retinoblastoma (1444, 2.8%, 1.5, 752/692); Renal (2310, 4.5%, 3.1, 1140/1168, 1 hermaphrodite & 1 unknown); Liver (732, 1.4%, 2.1, 428/304); Malignant bone (3122, 6.1%, 12.5, 1747/1375); Soft tissue sarcomas (3675, 7.2%, 7.3, 2047/1628); Germ cell (2933, 5.8%, 8.9, 1077/1849, 4 hermaphrodite, 3 unknown); Carcinoma & other malignant epithelial (1628, 3.2%, 13.1, 783/845); Other/non-specific malignant (370, 0.7%, 7.8, 184/186). Five-year survival rate was found as 71.1%. Conclusions: The survival rates for children and adolescents have improved to 70%, a figure comparable to that of other middle-income countries and reflective of the quality of pediatric cancer care in Turkey. This represents a successful pediatric cancer registry from a middle-income country, covering a period of over 20 years. It has become a valuable resource for stakeholders at both the national and international levels.

Prognostic Factors of Oligometastasis After Stereotactic Body Radiotherapy: The Real-World Utility of the European Society for Radiotherapy and Oncology/European Organisation for Research and Treatment of Cancer Classification.

The efficacy of local therapy for oligometastatic disease (OMD) remains unclear. This study aimed to evaluate the prognostic utility of the classification system for OMD and explore which groups may benefit from stereotactic body radiation therapy (SBRT). This single-center retrospective study included 45 patients (52 sites) with solid tumors and 1-3 extracranial oligometastases who underwent SBRT for all metastases at our institution between January 2018 and December 2021. OMD states were classified based on the European Society for Radiotherapy and Oncology (ESTRO) and the European Organisation for Research and Treatment of Cancer (EORTC) classification system. Local control (LC), overall survival (OS), and progression-free survival (PFS) for each group were analyzed using the Kaplan-Meier method. Acute and late adverse events (AEs) were evaluated according to the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0. The median follow-up period was 14 months (range: 0-48 months). The numbers of patients in the de novo (first diagnosis of OMD), repeat (previous history of OMD), and induced (previous history of polymetastatic disease) OMD groups were 15, 17, and 13, respectively. The LC rates at one year for the entire, de novo, repeat, and induced cohorts were 87.2%, 87.5%, 90.2%, and 83.9%, respectively (p=0.80). The one-year PFS rates for each group were 35.0%, 56.7%, and 29.9%, respectively (p=0.58). The one-year OS rates for each group were 80.0%, 86.2%, and 80.8%, respectively (p=0.50). Grade 2 or 3 AEs occurred in five patients (10.4%). No grade 4 or 5 AEs were observed. SBRT is safe and highly effective for local control. Patients with repeat OMD demonstrated a trend of longer PFS, suggesting that this subgroup may benefit from local therapy at metastatic sites.

Automatic Classification of Cervical Cells Using Deep Learning Methods

Cervical cancer, second only to breast cancer, is one of the cancers leading to cause death among women. Cervical cancer is a cancer that forms in the cells of the cervix, which is the lower section of the uterus that connects the uterus to the pelvis to the vaginal area. Various forms of the papilloma virus (HPV), a sexually transmitted infection that plays a role in cervical cancer and plays a critical part in most cases. The risk of cervical cancer developing can be reduced by undergoing screenings and receiving a vaccination that protects against HPV infection Cancer prevention is important. Most of the time, this is accomplished by checking the transformation zones. Cervical pre-cancerous stages can be observed in three different types, and all can transfigure into cancer. As a result, it's crucial to screen cervical anomalies sensibly and have a reliable process to determine if a cervix is normal (healthy) or pre-cancerous. Presently, the test being carried is a Pap smear test, commonly referred as a Pap test, which is a cervical screening procedure. It examines the cervix for the presence of pre - cancerous or cancerous cells. At present times deep learning is becoming more important alternative for cancer screening. A cervical cancer detection and classification system based on CNN has been proposed. Deep-learned features are acquired using the CNNs mode

The Effect of Intratumor Heterogeneity in Pancreatic Ductal Adenocarcinoma Progression and Treatment.

Pancreatic cancer is one of the most lethal malignancies. Even though many substantial improvements in the survival rates for other major cancer forms were made, pancreatic cancer survival rates have remained relatively unchanged since the 1960s. Even more, no standard classification system for pancreatic cancer is based on cellular biomarkers. This review will discuss and provide updates about the role of stem cells in the progression of PC, the genetic changes associated with it, and the promising biomarkers for diagnosis. The search process used PubMed, Cochrane Library, and Scopus databases to identify the relevant and related articles. Articles had to be published in English to be considered. The increasing number of studies in recent years has revealed that the diversity of cancer-associated fibroblasts is far greater than previously acknowledged, which highlights the need for further research to better understand the various cancer-associated fibroblast subpopulations. Despite the huge diversity in pancreatic cancer, some common features can be noted to be shared among patients. Mutations involving CDKN2, P53, and K-RAS can be seen in a big number of patients, for example. Similarly, some patterns of genes and biomarkers expression and the level of their expression can help in predicting cancer behavior such as metastasis and drug resistance. The current trend in cancer research, especially with the advancement in technology, is to sequence everything in hopes of finding disease-related mutations. Optimizing pancreatic cancer treatment requires clear classification, understanding CAF roles, and exploring stroma reshaping approaches.

Development and Validation of Adaptable Skin Cancer Classification System Using Dynamically Expandable Representation.

Skin cancer is a prevalent type of malignancy, necessitating efficient diagnostic tools. This study aimed to develop an automated skin lesion classification model using the dynamically expandable representation (DER) incremental learning algorithm. This algorithm adapts to new data and expands its classification capabilities, with the goal of creating a scalable and efficient system for diagnosing skin cancer. The DER model with incremental learning was applied to the HAM10000 and ISIC 2019 datasets. Validation involved two steps: initially, training and evaluating the HAM10000 dataset against a fixed ResNet-50; subsequently, performing external validation of the trained model using the ISIC 2019 dataset. The model's performance was assessed using precision, recall, the F1-score, and area under the precision-recall curve. The developed skin lesion classification model demonstrated high accuracy and reliability across various types of skin lesions, achieving a weighted-average precision, recall, and F1-score of 0.918, 0.808, and 0.847, respectively. The model's discrimination performance was reflected in an average area under the curve (AUC) value of 0.943. Further external validation with the ISIC 2019 dataset confirmed the model's effectiveness, as shown by an AUC of 0.911. This study presents an optimized skin lesion classification model based on the DER algorithm, which shows high performance in disease classification with the potential to expand its classification range. The model demonstrated robust results in external validation, indicating its adaptability to new disease classes.

Enhancing Skin Cancer Classification on the PH2 Dataset Through Transfer Learning Technique

Skin, the largest organ of the human body, serves as a crucial barrier against external threats. Among the myriad skin diseases, melanoma, or skin cancer, stands out as one of the most perilous and lethal conditions. However, its prognosis dramatically improves when detected early. The advent of advanced diagnostic imaging methods has mitigated the risks associated with cancer treatment, facilitating precise diagnoses and enhancing treatment efficacy. The development and evaluation of image processing algorithms for medical image analysis heavily rely on the availability of medical images. In this study, we utilize dermoscopic images sourced from the PH2 database for analysis. Our results demonstrate that the Skin Cancer Classification (SCC) system, employing Convolutional Neural Networks (CNN), outperforms traditional methods in terms of accuracy (98.5%), specificity (100%), and sensitivity (97.5%) across both stages of evaluation. From the analysis, it is evident that the superiority of CNN-based SCC systems in accurately diagnosing skin cancer. This paper underscores the significance of leveraging advanced image processing techniques for medical image analysis, paving the way for reliable skin cancer classification systems with potential clinical applicability.

A Novel Assessment of Lung Cancer Classification System Using Binary Grasshopper with Artificial Bee Optimisation Algorithm with Double Deep Neural Network Classifier

A Novel Assessment of Lung Cancer Classification System Using Binary Grasshopper with Artificial Bee Optimisation Algorithm with Double Deep Neural Network Classifier