Multiclass Cancer Classification Research Articles

DAX-Net: A dual-branch dual-task adaptive cross-weight feature fusion network for robust multi-class cancer classification in pathology images

Background and ObjectiveMulti-class cancer classification has been extensively studied in digital and computational pathology due to its importance in clinical decision-making. Numerous computational tools have been proposed for various types of cancer classification. Many of them are built based on convolutional neural networks. Recently, Transformer-style networks have shown to be effective for cancer classification. Herein, we present a hybrid design that leverages both convolutional neural networks and transformer architecture to obtain superior performance in cancer classification. MethodsWe propose a dual-branch dual-task adaptive cross-weight feature fusion network, called DAX-Net, which exploits heterogeneous feature representations from the convolutional neural network and Transformer network, adaptively combines them to boost their representation power, and conducts cancer classification as categorical classification and ordinal classification. For an efficient and effective optimization of the proposed model, we introduce two loss functions that are tailored to the two classification tasks. ResultsTo evaluate the proposed method, we employed colorectal and prostate cancer datasets, of which each contains both in-domain and out-of-domain test sets. For colorectal cancer, the proposed method obtained an accuracy of 88.4%, a quadratic kappa score of 0.945, and an F1 score of 0.831 for the in-domain test set, and 84.4%, 0.910, and 0.768 for the out-of-domain test set. For prostate cancer, it achieved an accuracy of 71.6%, a kappa score of 0.635, and an F1 score of 0.655 for the in-domain test set, 79.2% accuracy, 0.721 kappa score, and 0.686 F1 score for the first out-of-domain test set, and 58.1% accuracy, 0.564 kappa score, and 0.493 F1 score for the second out-of-domain test set. It is worth noting that the performance of the proposed method outperformed other competitors by significant margins, in particular, with respect to the out-of-domain test sets. ConclusionsThe experimental results demonstrate that the proposed method is not only accurate but also robust to varying conditions of the test sets in comparison to several, related methods. These results suggest that the proposed method can facilitate automated cancer classification in various clinical settings.

Genetic Clustering Algorithm-Based Feature Selection and Divergent Random Forest for Multiclass Cancer Classification Using Gene Expression Data

AbstractComputational identification and classification of clinical disorders gather major importance due to the effective improvement of machine learning methodologies. Cancer identification and classification are essential clinical areas to address, where accurate classification for multiple types of cancer is still in a progressive stage. In this article, we propose a multiclass cancer classification model that categorizes the five different types of cancers using gene expression data. To perform efficient analysis of the available clinical data, we propose feature selection and classification methods. We propose a genetic clustering algorithm (GCA) for optimal feature selection from the RNA-gene expression data, consisting of 801 samples belonging to the five major classes of cancer. The proposed feature selection method reduces the 1621 gene expressions into a cluster of 21 features. The optimum feature set acts as input data to the proposed divergent random forest. Based on the features computed, the proposed classifier categorizes the data samples into 5 different classes of cancers, including breast cancer, colon cancer, kidney cancer, lung cancer, and prostate cancer. The proposed divergent random forest provided performance improvisation in terms of accuracy with 95.21%, specificity with 93%, and sensitivity with 94.29% which outperformed all the other existing multiclass classification algorithms.

An efficient multi-class classification of skin cancer using optimized vision transformer.

Skin cancer is a pervasive and deadly disease, prompting a surge in research efforts towards utilizing computer-based techniques to analyze skin lesion images to identify malignancies. This paper introduces an optimized vision transformer approach for effectively classifying skin tumors. The methodology begins with a pre-processing step aimed at preserving color constancy, eliminating hair artifacts, and reducing image noise. Here, a combination of techniques such as piecewise linear bottom hat filtering, adaptive median filtering, Gaussian filtering, and an enhanced gradient intensity method is used for pre-processing. Afterwards, the segmentation phase is initiated using the self-sparse watershed algorithm on the pre-processed image. Subsequently, the segmented image is passed through a feature extraction stage where the hybrid Walsh-Hadamard Karhunen-Loeve expansion technique is employed. The final step involves the application of an improved vision transformer for skin cancer classification. The entire methodology is implemented using the Python programming language, and the International Skin Imaging Collaboration (ISIC) 2019 database is utilized for experimentation. The experimental results demonstrate remarkable performance with the different performance metrics is accuracy 99.81%, precision 96.65%, sensitivity 98.21%, F-measure 97.42%, specificity 99.88%, recall 98.21%, Jaccard coefficient 98.54%, and Mathew's correlation coefficient (MCC) 98.89%. The proposed methodology outperforms the existing methodology.

GeneNet: Transfer learning-based hybrid African buffalo optimization with genetic algorithm for gene expression based cancer classification

Gene therapy is one of the advanced medical diagnosis environments, which is used to identify the solutions to various cancers by identifying the perfect gene expressions. The computer aided detection of gene expressions is a research problem, where cancer type is identified by artificial intelligence methods. However, the conventional machine learning models were failed to provide the accurate cancer classification and resulted in poor quantitative performance. Therefore, this work focuses on the implementation of gene expression-based cancer classification (GECC) network, here after referred as GeneNet. Initially, the proposed GeneNet performs the data preprocessing to normalize the gene-expression data by replacing the unknown characters and missing symbols. Then, the visual geometry group developed VGG16 model for extraction of correlated, and structural features from pre-processed dataset. In addition, hybrid African buffalo optimization with genetic algorithm (HABO-GA) is used to select the optimal features through natural inspired hunting (searching) properties. Finally, low complex convolutional neural network (LC-CNN) model is trained with HABO-GA features, which also performs the multi-class cancer classification from test data. The simulations are conducted on publicly available GECC datasets such as lymphography, ovarian, leukemia, lung, and prostate cancers discloses the superiority of proposed GeneNetas compared to state-of-the-art approaches with 86.6 %, 100 %, 76.92 %, 100 %, and 100 % of accuracy, respectively.

MSCDNet-based multi-class classification of skin cancer using dermoscopy images.

Skin cancer is a life-threatening disease, and early detection of skin cancer improves the chances of recovery. Skin cancer detection based on deep learning algorithms has recently grown popular. In this research, a new deep learning-based network model for the multiple skin cancer classification including melanoma, benign keratosis, melanocytic nevi, and basal cell carcinoma is presented. We propose an automatic Multi-class Skin Cancer Detection Network (MSCD-Net) model in this research. The study proposes an efficient semantic segmentation deep learning model "DenseUNet" for skin lesion segmentation. The semantic skin lesions are segmented by using the DenseUNet model with a substantially deeper network and fewer trainable parameters. Some of the most relevant features are selected using Binary Dragonfly Algorithm (BDA). SqueezeNet-based classification can be made in the selected features. The performance of the proposed model is evaluated using the ISIC 2019 dataset. The DenseNet connections and UNet links are used by the proposed DenseUNet segmentation model, which produces low-level features and provides better segmentation results. The performance results of the proposed MSCD-Net model are superior to previous research in terms of effectiveness and efficiency on the standard ISIC 2019 dataset.

CaMeL-Net: Centroid-aware metric learning for efficient multi-class cancer classification in pathology images

Background and objectiveCancer grading in pathology image analysis is a major task due to its importance in patient care, treatment, and management. The recent developments in artificial neural networks for computational pathology have demonstrated great potential to improve the accuracy and quality of cancer diagnosis. These improvements are generally ascribable to the advance in the architecture of the networks, often leading to increase in the computation and resources. In this work, we propose an efficient convolutional neural network that is designed to conduct multi-class cancer classification in an accurate and robust manner via metric learning. MethodsWe propose a centroid-aware metric learning network for an improved cancer grading in pathology images. The proposed network utilizes centroids of different classes within the feature embedding space to optimize the relative distances between pathology images, which manifest the innate similarities/dissimilarities between them. For improved optimization, we introduce a new loss function and a training strategy that are tailored to the proposed network and metric learning. ResultsWe evaluated the proposed approach on multiple datasets of colorectal and gastric cancers. For the colorectal cancer, two different datasets were employed that were collected from different acquisition settings. the proposed method achieved an accuracy, F1-score, quadratic weighted kappa of 88.7%, 0.849, and 0.946 for the first dataset and 83.3%, 0.764, and 0.907 for the second dataset, respectively. For the gastric cancer, the proposed method obtained an accuracy of 85.9%, F1-score of 0.793, and quadratic weighted kappa of 0.939. We also found that the proposed method outperforms other competing models and is computationally efficient. ConclusionsThe experimental results demonstrate that the prediction results by the proposed network are both accurate and reliable. The proposed network not only outperformed other related methods in cancer classification but also achieved superior computational efficiency during training and inference. The future study will entail further development of the proposed method and the application of the method to other problems and domains.

Classification of skin cancer using adjustable and fully convolutional capsule layers

Early and accurate diagnosis plays a significant role in increasing the survival rate of patients with skin cancer. Therefore, automated methods with convolutional neural networks have been developed to help dermatologists in making their decision about the disease. However, these kinds of networks have these two important shortcomings that cause low performance when real-word images are used: (i) They need huge number of training datasets, and (ii) they lead to the loss of information because of the pooling process. Although, capsule networks have been proposed recently to solve these issues and to achieve classification of skin cancers with high accuracy, they have their own defects or disadvantages. Therefore, in this work, a new neural network constructed by adjustable and convolutional capsule layers is proposed. In the adjustable capsule layer, the spatial relationships of capsule vectors are encoded with a constant learnable bias. The capsule vectors are used in a sub-network to obtain adjustable values. The proposed network can learn encoding and modelling of the spatial relation between capsule vectors and preserves the vectors’ orientations. The main contributions of this work are as follows: (i) A novel network including capsule layers is presented. (ii) The performance of the proposed network in the multi-class classification of skin cancer is presented. (iii) Comparative evaluations of the capsule networks applied for the classification of skin cancers are presented. Experiments indicate that the proposed network is promising in the classification of seven kinds of skin cancer (with an accuracy of 95.24%).

Transfer Learning Based Lightweight Ensemble Model for Imbalanced Breast Cancer Classification.

Automated classification of breast cancer can often save lives, as manual detection is usually time-consuming & expensive. Since the last decade, deep learning techniques have been most widely used for the automatic classification of breast cancer using histopathology images. This paper has performed the binary and multi-class classification of breast cancer using a transfer learning-based ensemble model. To analyze the correctness and reliability of the proposed model, we have used an imbalance IDC dataset, an imbalance BreakHis dataset in the binary class scenario, and a balanced BACH dataset for the multi-class classification. A lightweight shallow CNN model with batch normalization technology to accelerate convergence is aggregated with lightweight MobileNetV2 to improve learning and adaptability. The aggregation output is fed into a multilayer perceptron to complete the final classification task. The experimental study on all three datasets was performed and compared with the recent works. We have fine-tuned three different pre-trained models (ResNet50, InceptionV4, and MobilNetV2) and compared it with the proposed lightweight ensemble model in terms of execution time, number of parameters, model size, etc. In both the evaluation phases, it is seen that our model outperforms in all three datasets.

Exploration of the Relevance of MicroRNA Signatures for Cancer Detection and Multiclass Cancer Classification

miRNA expression profiles are heterogeneously expressed among cancer types, with miRNAs serving as highly tissue specific tumor suppressors and oncogenes. Machine learning methodologies have been used to develop high performance pan-cancer classification models and identify potentially novel miRNA biomarkers for clinical investigation. However, it is important to understand how such data science techniques correlate to established biological processes to advance integration into clinical environments. This research aims to assess how the top miRNA features selected by machine learning models relate to clinically and biologically verified miRNA biomarkers. We developed Support Vector Machine and Random Forest machine learning models for cancer classification, iteratively adding cancer classes to the multiclass models. The relationship between the selected top features (miRNAs) and clinically verified miRNA biomarkers was assessed through percent relevance, i.e., the number of verified miRNAs vs the number of selected features. We found that as the number of cancer classes increased, the performance metrics decreased, yet the percentage relevance of the miRNA feature selection signature slightly increased before stabilizing. Additionally, after conducting principal component analysis, the non-cancer tissues from all samples had very similar expression visualizations, while all cancerous tissues had unique profiles. The results indicated that models with a greater number of cancer classes shift towards focusing on cancer-diverse miRNAs of greater relevance with characterized functionality. This work suggests that miRNAs may be highly unique to specific cancerous tissues and can be strong biomarkers for detection and classification, but current verified biomarkers fall toward more cancer-wide miRNAs when detecting cancer.

An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

Breast cancer is one of the leading cancers among women. It has the second-highest mortality rate in women after lung cancer. Timely detection, especially in the early stages, can help increase survival rates. However, manual diagnosis of breast cancer is a tedious and time-consuming process, and the accuracy of detection is reliant on the quality of the images and the radiologist’s experience. However, computer-aided medical diagnosis has recently shown promising results, leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early stages. The research presented in this paper is focused on the multi-class classification of breast cancer. The deep transfer learning approach has been utilized to train the deep learning models, and a pre-processing technique has been used to improve the quality of the ultrasound dataset. The proposed technique utilizes two deep learning models, MobileNetV2 and DenseNet201, for the composition of the deep ensemble model. Deep learning models are fine-tuned along with hyperparameter tuning to achieve better results. Subsequently, entropy-based feature selection is used. Breast cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%, while the sensitivity and F1 score were 96.87% and 96.76%, respectively. The performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.

BreaST-Net: Multi-Class Classification of Breast Cancer from Histopathological Images Using Ensemble of Swin Transformers

Breast cancer (BC) is one of the deadly forms of cancer, causing mortality worldwide in the female population. The standard imaging procedures for screening BC involve mammography and ultrasonography. However, these imaging procedures cannot differentiate subtypes of benign and malignant cancers. Here, histopathology images could provide better sensitivity toward benign and malignant cancer subtypes. Recently, vision transformers have been gaining attention in medical imaging due to their success in various computer vision tasks. Swin transformer (SwinT) is a variant of vision transformer that works on the concept of non-overlapping shifted windows and is a proven method for various vision detection tasks. Thus, in this study, we investigated the ability of an ensemble of SwinTs in the two-class classification of benign vs. malignant and eight-class classification of four benign and four malignant subtypes, using an openly available BreaKHis dataset containing 7909 histopathology images acquired at different zoom factors of 40×, 100×, 200×, and 400×. The ensemble of SwinTs (including tiny, small, base, and large) demonstrated an average test accuracy of 96.0% for the eight-class and 99.6% for the two-class classification, outperforming all the previous works. Thus, an ensemble of SwinTs could identify BC subtypes using histopathological images and may lead to pathologist relief.

A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images

In 2019, the world experienced the rapid outbreak of the Covid-19 pandemic creating an alarming situation worldwide. The virus targets the respiratory system causing pneumonia with other symptoms such as fatigue, dry cough, and fever which can be mistakenly diagnosed as pneumonia, lung cancer, or TB. Thus, the early diagnosis of COVID-19 is critical since the disease can provoke patients’ mortality. Chest X-ray (CXR) is commonly employed in healthcare sector where both quick and precise diagnosis can be supplied. Deep learning algorithms have proved extraordinary capabilities in terms of lung diseases detection and classification. They facilitate and expedite the diagnosis process and save time for the medical practitioners. In this paper, a deep learning (DL) architecture for multi-class classification of Pneumonia, Lung Cancer, tuberculosis (TB), Lung Opacity, and most recently COVID-19 is proposed. Tremendous CXR images of 3615 COVID-19, 6012 Lung opacity, 5870 Pneumonia, 20,000 lung cancer, 1400 tuberculosis, and 10,192 normal images were resized, normalized, and randomly split to fit the DL requirements. In terms of classification, we utilized a pre-trained model, VGG19 followed by three blocks of convolutional neural network (CNN) as a feature extraction and fully connected network at the classification stage. The experimental results revealed that our proposed VGG19 + CNN outperformed other existing work with 96.48 % accuracy, 93.75 % recall, 97.56 % precision, 95.62 % F1 score, and 99.82 % area under the curve (AUC). The proposed model delivered superior performance allowing healthcare practitioners to diagnose and treat patients more quickly and efficiently.

Multi-Class Classification of Breast Cancer Using 6B-Net with Deep Feature Fusion and Selection Method.

Breast cancer has now overtaken lung cancer as the world’s most commonly diagnosed cancer, with thousands of new cases per year. Early detection and classification of breast cancer are necessary to overcome the death rate. Recently, many deep learning-based studies have been proposed for automatic diagnosis and classification of this deadly disease, using histopathology images. This study proposed a novel solution for multi-class breast cancer classification from histopathology images using deep learning. For this purpose, a novel 6B-Net deep CNN model, with feature fusion and selection mechanism, was developed for multi-class breast cancer classification. For the evaluation of the proposed method, two large, publicly available datasets, namely, BreaKHis, with eight classes containing 7909 images, and a breast cancer histopathology dataset, containing 3771 images of four classes, were used. The proposed method achieves a multi-class average accuracy of 94.20%, with a classification training time of 226 s in four classes of breast cancer, and a multi-class average accuracy of 90.10%, with a classification training time of 147 s in eight classes of breast cancer. The experimental outcomes show that the proposed method achieves the highest multi-class average accuracy for breast cancer classification, and hence, the proposed method can effectively be applied for early detection and classification of breast cancer to assist the pathologists in early and accurate diagnosis of breast cancer.

PanClassif: Improving pan cancer classification of single cell RNA-seq gene expression data using machine learning.

Cancer is one of the major causes of human death per year. In recent years, cancer identification and classification using machine learning have gained momentum due to the availability of high throughput sequencing data. Using RNA-seq, cancer research is blooming day by day and new insights of cancer and related treatments are coming into light. In this paper, we propose PanClassif, a method that requires a very few and effective genes to detect cancer from RNA-seq data and is able to provide performance gain in several wide range machine learning classifiers. We have taken 22 types of cancer samples from The Cancer Genome Atlas (TCGA) having 8287 cancer samples and 680 normal samples. Firstly, PanClassif uses k-Nearest Neighbour (k-NN) smoothing to smooth the samples to handle noise in the data. Then effective genes are selected by Anova based test. For balancing the train data, PanClassif applies an oversampling method, SMOTE. We have performed comprehensive experiments on the datasets using several classification algorithms. Experimental results shows that PanClassif outperform existing state-of-the-art methods available and shows consistent performance for two single cell RNA-seq datasets taken from Gene Expression Omnibus (GEO). PanClassif improves performances of a wide variety of classifiers for both binary cancer prediction and multi-class cancer classification. PanClassif is available as a python package (https://pypi.org/project/panclassif/). All the source code and materials of PanClassif are available at https://github.com/Zwei-inc/panclassif.

A Study on Multi-class Classification of Breast Cancer Images using Ensemble Network and Transfer Learning

Background: Breast cancer causes millions of deaths all over the world every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increase the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi-class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. Methods: The current paper presents an ensemble Convolutional neural network for multi-class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from the publicly available BreakHis dataset and classified into 8 classes. Results: The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification. Conclusion: In this paper, an approach for multi-class classification on the breast images for cancer detection is proposed. The proposed architecture can be a viable option for the classification of histopathology images.

LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images.

Liver cancer is one of the most common types of cancers in Asia with a high mortality rate. A common method for liver cancer diagnosis is the manual examination of histopathology images. Due to its laborious nature, we focus on alternate deep learning methods for automatic diagnosis, providing significant advantages over manual methods. In this paper, we propose a novel deep learning framework to perform multi-class cancer classification of liver hepatocellular carcinoma (HCC) tumor histopathology images which shows improvements in inference speed and classification quality over other competitive methods. The BreastNet architecture proposed by Togacar et al. shows great promise in using convolutional block attention modules (CBAM) for effective cancer classification in H&E stained breast histopathology images. As part of our experiments with this framework, we have studied the addition of atrous spatial pyramid pooling (ASPP) blocks to effectively capture multi-scale features in H&E stained liver histopathology data. We classify liver histopathology data into four classes, namely the non-cancerous class, low sub-type liver HCC tumor, medium sub-type liver HCC tumor, and high sub-type liver HCC tumor. To prove the robustness and efficacy of our models, we have shown results for two liver histopathology datasets-a novel KMC dataset and the TCGA dataset. Our proposed architecture outperforms state-of-the-art architectures for multi-class cancer classification of HCC histopathology images, not just in terms of quality of classification, but also in computational efficiency on the novel proposed KMC liver data and the publicly available TCGA-LIHC dataset. We have considered precision, recall, F1-score, intersection over union (IoU), accuracy, number of parameters, and FLOPs as metrics for comparison. The results of our meticulous experiments have shown improved classification performance along with added efficiency. LiverNet has been observed to outperform all other frameworks in all metrics under comparison with an approximate improvement of [Formula: see text] in accuracy and F1-score on the KMC and TCGA-LIHC datasets. To the best of our knowledge, our work is among the first to provide concrete proof and demonstrate results for a successful deep learning architecture to handle multi-class HCC histopathology image classification among various sub-types of liver HCC tumor. Our method shows a high accuracy of [Formula: see text] on the proposed KMC liver dataset requiring only 0.5739 million parameters and 1.1934 million floating point operations per second.

Classification of Breast Cancer using Histology images: Handcrafted and Pre-Trained Features Based Approach

Breast cancer has become a critical disease in women. The number of patients with breast cancer is quite high in India. It is of paramount importance to detect the disease in advance. Digital histopathology is one of the most advanced techniques for detection using machine learning. Artificial intelligence is going to be like a sunrise in the field of medicine. Deep neural networks have been successfully applied to the problem under consideration in the past. As, we know the feature extraction is one of the essential and crucial steps in case of classification. In this paper, we compare two approaches, first is feature extraction using traditional Handcrafted based and other is Transfer Learning based model (Pre-trained) for multiclass classification of Breast Cancer using Convolutional Neural Network (CNN) as a classifier. The models are trained using handcrafted features like Seeped Up Robust Features (SURF) and Dense Scale Invariant Feature Transform (DSIFT) techniques, later these extracted features are encoded by Locality Constrained Linear Coding method (LLC). In pre-trained model we have used VGG16, VGG19, ResNet50, GoogLeNet for feature extraction. The maximum accuracy for “SURF+CNN” is 92.88% for Handcrafted feature and in case of Pre-trained “GoogLeNet+ CNN” model gives 94%, both for 400X magnification factor.

Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer.

We apply for the first-time interpretable deep learning methods simultaneously to the most common skin cancers (basal cell carcinoma, squamous cell carcinoma and intraepidermal carcinoma) in a histological setting. As these three cancer types constitute more than 90% of diagnoses, we demonstrate that the majority of dermatopathology work is amenable to automatic machine analysis. A major feature of this work is characterising the tissue by classifying it into 12 meaningful dermatological classes, including hair follicles, sweat glands as well as identifying the well-defined stratified layers of the skin. These provide highly interpretable outputs as the network is trained to represent the problem domain in the same way a pathologist would. While this enables a high accuracy of whole image classification (93.6-97.9%), by characterising the full context of the tissue we can also work towards performing routine pathologist tasks, for instance, orientating sections and automatically assessing and measuring surgical margins. This work seeks to inform ways in which future computer aided diagnosis systems could be applied usefully in a clinical setting with human interpretable outcomes.

Multiclass cancer classification in fresh frozen and formalin-fixed paraffin-embedded tissue by DigiWest multiplex protein analysis

AbstractHistomorphology and immunohistochemistry are the most common ways of cancer classification in routine cancer diagnostics, but often reach their limits in determining the organ origin in metastasis. These cancers of unknown primary, which are mostly adenocarcinomas or squamous cell carcinomas, therefore require more sophisticated methodologies of classification. Here, we report a multiplex protein profiling-based approach for the classification of fresh frozen and formalin-fixed paraffin-embedded (FFPE) cancer tissue samples using the digital western blot technique DigiWest. A DigiWest-compatible FFPE extraction protocol was developed, and a total of 634 antibodies were tested in an initial set of 16 FFPE samples covering tumors from different origins. Of the 303 detected antibodies, 102 yielded significant correlation of signals in 25 pairs of fresh frozen and FFPE primary tumor samples, including head and neck squamous cell carcinomas (HNSC), lung squamous cell carcinomas (LUSC), lung adenocarcinomas (LUAD), colorectal adenocarcinomas (COAD), and pancreatic adenocarcinomas (PAAD). For this signature of 102 analytes (covering 88 total proteins and 14 phosphoproteins), a support vector machine (SVM) algorithm was developed. This allowed for the classification of the tissue of origin for all five tumor types studied here with high overall accuracies in both fresh frozen (90.4%) and FFPE (77.6%) samples. In addition, the SVM classifier reached an overall accuracy of 88% in an independent validation cohort of 25 FFPE tumor samples. Our results indicate that DigiWest-based protein profiling represents a valuable method for cancer classification, yielding conclusive and decisive data not only from fresh frozen specimens but also FFPE samples, thus making this approach attractive for routine clinical applications.Proteomic profiling may contribute to the analysis and classification of cancer. The authors applied the digital western blot technique DigiWest with a panel of 102 proteins and phosphoproteins in combination with a machine learning algorithm to classify the tissue origin of five common cancer types in fresh frozen and formalin-fixed paraffin-embedded tissue. DigiWest profiling represents a valuable method for cancer classification, yielding conclusive and decisive data, thus making this approach attractive for routine clinical applications.

Multi-Class Skin Cancer Detection and Classification Using Hybrid Features Extraction Techniques

Skin cancer is measured as one of the fatal types of cancer diseases in humans, among numerous kinds of malignancy. Current diagnostic classifications are lacking in finding an effective treatment. The effective and early stage treatment of skin disease can increase the survival rate of patients. Substantial investigative work has been developed to improve computer aided diagnosis system to detect cancer at early stage. However, early detection of skin cancer still requires better accuracy through experiment on digital skin lesion images as a multiclass classification, rather than using biopsy methods. This paper presents an intelligent framework to detect and classify four types of skin cancer. Before classification, noise removal from skin lesion is performed by gaussian filter. Textural and colour features are extracted from skin lesion to detect and classify cancer into four types. Support vector Machine is trained to classify Melanoma, Nevus, Basal and Squamous skin cancer types. Extensive experiments are performed on standard benchmark skin cancer images dataset with an improvement in accuracy of 92.41% after comparison with the well-known methods.