Diagnosis Of Skin Cancer Research Articles

Association of hydrochlorothiazide treatment compared to alternative diuretics with overall and skin cancer risk: a propensity-matched cohort study

Abstract Introduction Hydrochlorothiazide (HCT) has been suggested to induce photosensitivity, thereby increasing the incidence of skin cancers. After a pharmacovigilance alert, HCT was frequently withdrawn or substituted by other diuretics. The aim of this study was to compare the association of exposure to HCT with cancer risk versus alternative diuretics. Methods A retrospective cohort study was conducted based on data from the AOK PLUS, a large German statutory health insurance fund. Patients with HCT treatment were propensity score matched (PSM) to patients using non-HCT diuretics. Incidence of cancer of any kind and, specifically, skin cancer was assessed in both groups. Time to incident cancer diagnosis was evaluated and compared between the groups. Results A total of 199,708 patients were included in the final analysis (n=76,855 in the HCT-group; n=122,853 in the non-HCT-diuretics group). After PSM, 122,554 patients remained in the sample (n=61,277 for both groups, of which >96% had hypertension, mean age 73 years, 61% female). HCT treatment was associated with a lower incidence of cancer of any kind, compared with non-HCT diuretics (Incidence rate ratio (IRR) per 100 patient years 0.84 95% Confidence interval (CI): 0.82-0.87). HCT treatment was associated with a small albeit significantly higher IRR of skin cancer (1.14 95% CI: 1.06-1.24) with significant variances over time. Although numerically higher, the difference accounts to only 0.05 more skin cancer diagnoses in 100 patient years. Conclusions HCT treatment compared to alternative diuretics was associated with a lower all-cancer risk and was not associated with a meaningful increase in skin cancer risk in a large German population. Nevertheless, advice for skin protection is warranted in all patients taking thiazide or thiazide-like diuretics.HR of overall and skin cancerTime to cancer diagnosis - time to event

Automated Melanoma Skin Disease Detection Using the Spike Herd Search Algorithm

Melanoma is rare and can shorten a patient's life. It can be cured if found early. Early cost, pain and timing prediction affect patients. Thus, many researchers use Artificial Intelligence (AI) to identify Melanoma skin cancer. However, complicated skin lesions, illumination, and a lot of training data make diagonalising Melanoma skin cancer diagnosis tough. The Spiking Herd Search Learning (SHSL) approach for optimal feature selection-based classification promotes the category to overcome this issue. The method combines a unique hybrid metaheuristic algorithm, Horse herd Optimisation Algorithm (HOA), and Squirrel Search Optimisation (SSO) with deep hybrid learning as Spiking Neural Network (SNN) and Convolutional Neural Network (CNN) classifiers. In preprocessing, a Gaussian filter removes skin lesion noise using Image Processing techniques. The k-means clustering method performs the segmentation and excellent feature extraction techniques locate the lesion exactly. The Horse herd Optimisation Algorithm (HOA) and Squirrel Search Optimisation (SSO) algorithms and techniques are used toimprove Spike Neural Network classification (SNN) accurate features. Finally, using the DermIS Image dataset, the SHSL algorithm is evaluated and compared to other methods. The proposed method detects Melanoma Skin Disease with 98.91% accuracy.

Skin cancer diagnosis (SCD) using Artificial Neural Network (ANN) and Improved Gray Wolf Optimization (IGWO)

Skin Cancer (SC) is one of the most dangerous types of cancer and if not treated in time, it can threaten the patient’s life. With early diagnosis of this disease, treatment methods can be used more effectively and the progression of the disease can be prevented. Machine Learning (ML) techniques can be utilized as a useful and efficient tool for SCD. So far, various methods for automatic SCD based on ML techniques have been presented; However, this research field still requires the application of optimal and efficient models to increase the accuracy of SCD. Therefore, in this article, a new method for SCD using a combination of optimization techniques and Artificial Neural Networks (ANNs) is presented. The proposed method includes four steps: pre-processing, segmentation, feature extraction, and classification. Image segmentation for identifying the lesion region is performed using a Kohonen neural network, where the identified region of interest (ROI) is enhanced using the Greedy Search Algorithm (GSA). The proposed method, uses a Convolutional Neural Network (CNN) for extracting features from ROIs. Also, to classify features, an ANN is used, and by the Improved Gray Wolf Optimization (IGWO) algorithm, the number of neurons and weight vector are adjusted. In this method, a probabilistic model is used to improve the convergence speed of the GWO algorithm. Based on the evaluation results, using the IGWO model to optimize the structure and weight vector of the ANN can be effective in increasing the diagnosis accuracy by at least 5%. The results of implementing the proposed method and comparing its performance with previous methods also show that this method can diagnose SC in the ISIC-2016 and ISIC-2017 databases with an average accuracy of 97.09 and 95.17%, respectively; which improves accuracy by at least 0.5% compared to other methods.

Modelling of Whale Optimization Algorithm with Deep Learning for Skin Cancer Diagnosis on Dermoscopic Images

Skin cancer diagnoses through the analysis of dermoscopy images have undergone a revolutionary change with the incorporation of advanced Machine Learning (ML) approaches. By using contemporary models such as Convolutional Neural Network (CNN) and Deep Learning (DL) structures, this technique allows automated and accurate detection of malignant and benign skin tumours. This model learns intricate patterns and features directly from the image, which allows them to distinguish between different types of skin tumours based on colour, texture, and structural characteristics. This manuscript presents the Whale Optimization Algorithm with Deep Learning for Skin Cancer Diagnoses on Dermoscopy Images (WOADL-SCDDI) technique. The objective of the WOADL-SCDDI method is to analyze the Dermoscopic images for classifying and recognizing skin tumours. In the proposed WOADL-SCDDI technique, median filtering-based preprocessing and SegNet-based segmentation are involved. In addition, the WOADL-SCDDI technique offers a ResNet50 model for feature extraction with WOA-based hyperparameter tuning. Finally, Quasi Recurrent Neural Network (QRNN) model can be applied for skin tumour recognition and classification process. A comprehensive set of simulations have been conducted to highlight the better efficiency of the WOADL-SCDDI technique. The accomplished outcomes portrayed the improvements of the WOADL-SCDDI method compared to existing DL models.

A powerful deep learning method for skin cancer detection

<p>The authors discussed the issues posed by our study’s inability to accurately diagnose skin cancer and distinguish between various skin growths, especially without the aid of cutting-edge medical technology and highly skilled diagnosticians. To that purpose, the authors have developed a deep learning (DL) method that can recognise skin cancer from images. This study investigates the use of a convolutional neural network (CNN) and the Keras Sequential-application programming interface (API) to detect cancer in seven different types of chronic lesions. The researchers used the HAM10000 dataset, which is openly available. 10,015 skin growth images with annotations are included in this dataset. The authors used several data pre-processing techniques after reading the data but before training our model. The authors provide pre-trained data for comparison and reliability assessments. Examples of transfer learning models that are used for comparison include ResNet50, DenseNet121, and VGG11. In the area of skin growth classification for skin cancer diagnosis, this helps in the identification of improved DL application procedures. Over 97.12% of the time, our suggested model accurately predicts the sort of skin that will develop.</p>

Polarimetric imaging combining optical parameters for classification of mice non-melanoma skin cancer tissue using machine learning

Polarimetric imaging systems combining machine learning is emerging as a promising tool for the support of diagnosis and intervention decision-making processes in cancer detection/staging. A present study proposes a novel method based on Mueller matrix imaging combining optical parameters and machine learning models for classifying the progression of skin cancer based on the identification of three different types of mice skin tissues: healthy, papilloma, and squamous cell carcinoma. Three different machine learning algorithms (K-Nearest Neighbors, Decision Tree, and Support Vector Machine (SVM)) are used to construct a classification model using a dataset consisting of Mueller matrix images and optical properties extracted from the tissue samples. The experimental results show that the SVM model is robust to discriminate among three classes in the training stage and achieves an accuracy of 94 % on the testing dataset. Overall, it is provided that polarimetric imaging systems and machine learning algorithms can dynamically combine for the reliable diagnosis of skin cancer.

Impact of COVID-19 Pandemic on Cutaneous Squamous Cell Carcinoma: A Single-Centre Study of Epidemiologic, Clinic and Histopathological Factors

BackgroundThe COVID-19 pandemic may have adversely affected the early diagnosis of skin cancer. ObjectiveTo compare epidemiological, clinical and histopathological characteristics in patients undergoing cutaneous squamous cell carcinoma (SCC) surgery before and after the beginning of the pandemic. Material & methodsWe conducted a cross-sectional study including two case series: (1) patients operated on for SCC in the year after the first state of alarm in Spain (15 March 2020), and (2) patients with SCC operated on in the previous year. Epidemiological, clinical and histopathological variables, tumour stage and risk grade were collected. Results248 patients were included (127 undergoing surgery before the pandemic and 121 after the pandemic). After the beginning of the pandemic, the percentage of high-risk SCC significantly increased from 35.3% to 46.2% (p=0.011). However, no significant differences were found in thickness, perineural invasion or metastases. ConclusionsAlthough there has not been a significant reduction in the number of SCC operated on after the pandemic, there has been a significant increase in high-risk SCC. All this could lead to an increase in skin cancer mortality in the future.

Internet of Things-Assisted Smart Skin Cancer Detection Using Metaheuristics with Deep Learning Model.

Internet of Things (IoT)-assisted skin cancer recognition integrates several connected devices and sensors for supporting the primary analysis and monitoring of skin conditions. A preliminary analysis of skin cancer images is extremely difficult because of factors such as distinct sizes and shapes of lesions, differences in color illumination, and light reflections on the skin surface. In recent times, IoT-based skin cancer recognition utilizing deep learning (DL) has been used for enhancing the early analysis and monitoring of skin cancer. This article presents an optimal deep learning-based skin cancer detection and classification (ODL-SCDC) methodology in the IoT environment. The goal of the ODL-SCDC technique is to exploit metaheuristic-based hyperparameter selection approaches with a DL model for skin cancer classification. The ODL-SCDC methodology involves an arithmetic optimization algorithm (AOA) with the EfficientNet model for feature extraction. For skin cancer detection, a stacked denoising autoencoder (SDAE) classification model has been used. Lastly, the dragonfly algorithm (DFA) is utilized for the optimal hyperparameter selection of the SDAE algorithm. The simulation validation of the ODL-SCDC methodology has been tested on a benchmark ISIC skin lesion database. The extensive outcomes reported a better solution of the ODL-SCDC methodology compared with other models, with a maximum sensitivity of 97.74%, specificity of 99.71%, and accuracy of 99.55%. The proposed model can assist medical professionals, specifically dermatologists and potentially other healthcare practitioners, in the skin cancer diagnosis process.

A Comprehensive Survey of Convolutional Neural Networks for Skin Cancer Classification and Prediction

Skin cancer, a prevalent and potentially fatal condition, requires early detection and precise classification to ensure effective treatment. In recent years, there has been a significant rise in the popularity of Convolutional Neural Networks (CNNs) prominence as a robust solution for image processing and analysis, significantly surpassing conventional techniques in skin cancer prediction and classification. This survey paper offers a thorough examination of CNNs and their diverse applications in diagnosing skin cancer, emphasizing their benefits, existing obstacles, and potential avenues for future research.

A Comprehensive Study of Different Skin Cancer Detection Models Using Deep Learning Techniques

Skin cancer is a highly prevalent disease that exhibits rapid growth worldwide. The timely identification and accurate diagnosis of skin cancer are of paramount importance in the context of preventive measures. The identification of skin cancer in its early stages poses a significant challenge for dermatologists. In recent years, machine learning techniques have been widely employed in both supervised and unsupervised learning tasks to address this issue. In this study, different existing techniques to detect different types of skin cancers and the evaluation metrics to assess their performance are dealt with.

Modeling of Dragonfly Algorithm with Deep Learning for Skin Cancer Diagnosis on Dermoscopic Images

Modeling of Dragonfly Algorithm with Deep Learning for Skin Cancer Diagnosis on Dermoscopic Images

SKIN CANCER IMAGE SEGMENTATION USING DEEP LEARNING

Because skin cancer is a frequent and possibly lethal illness, early identification is crucial for surgical treatment. Personal dermatoscopy is crucial in the perception of skin cancer. Recent research on deep literacy techniques has shown astonishing success in automating the segmentation of skin lesions, assisting in the early diagnosis and treatment of skin cancer. This goal provides a summary of improvements in skin cancer image segmentation that are based on deep literacy. The goal of this project is to create a dependable and effective deep literacy frame for segmenting skin lesions from the dermoscopic image collection. Convolutional neural networks (CNNs), which are deep learning models that are incredibly effective at detecting daedal patterns in images, are used in the proposed model. The training data consists of a distinct collection of annotated dermoscopy images that contains nonidentical forms of skin cancer such as carcinoma, rudimentary cell melanoma, and scaled cell melanoma. To precisely describe the size of the skin abnormality, segmentation entails identifying the lesion boundaries. Transfer literacy is practiced, which entails using pre-trained convolutional neural network infrastructures, to improve the model's interpretation. Transfer literacy improves segmentation interpretation by using features discovered from large datasets. Key Words: CNN, Skin cancer, Dermoscopic, Image segmentation, Network

An Interpretable Skin Cancer Classification Using Optimized Convolutional Neural Network for a Smart Healthcare

Skin cancer is a prevalent and life-threatening disease worldwide, making early and accurate diagnosis crucial for effective treatment. In the era of smart healthcare, the integration of artificial intelligence and computer-aided diagnosis systems has shown promise in improving the diagnostic accuracy and interpretability of skin cancer classification. This project presents an innovative approach for the interpretable classification of skin cancer using an optimized Convolutional Neural Network (CNN). The proposed system leverages deep learning techniques to analyze dermatoscopic images, offering a non-invasive and efficient solution for early skin cancer detection. Through careful optimization of the CNN architecture, feature extraction, and training parameters, our model achieves enhanced classification performance while maintaining a high degree of interpretability. Furthermore, the system incorporates advanced visualization and explanation techniques to provide clinicians and patients with transparent insights into the decision-making process of the CNN. This interpretability is crucial for building trust in the automated diagnostic system and facilitating effective collaboration between AI and healthcare professionals. The project's results demonstrate the potential for improved skin cancer diagnosis through a transparent and optimized CNN model, contributing to the advancement of smart healthcare. The combination of AI technology and interpretability not only enhances the accuracy of skin cancer classification but also ensures that the decision-making process is comprehensible and actionable for medical practitioners, ultimately leading to better patient outcomes. Key Words: Skin cancer, Healthcare, Artificial Intelligence, Deep Learning, OpenCV, RNN

Özel Derin Konvolüsyonel Sinir Ağı Kullanarak Cilt Kanseri Tanıma

Skin cancer is a common form of cancer that affects millions of people worldwide. Early detection and accurate diagnosis of skin cancer are crucial for effective treatment and management of the disease. There has been a growing interest in using deep learning techniques and computer vision algorithms to develop automated skin cancer detection systems in recent years. Among these techniques, convolutional neural networks (CNN) have shown remarkable performance in detecting and classifying skin lesions. This paper presents a comprehensive study using CNN and deep learning techniques for skin cancer detection using the International Skin Imaging Collaboration (ISIC) dataset. The proposed architecture is a compact deep CNN that is trained using a dataset of benign and malignant skin lesion images. The proposed architecture has achieved 84.8% accuracy, 83.8% TPR, 83.7% TNR, 81.6% F1-score and 80.5% precision for performance evaluation. The experimental results show promising results for the accurate and efficient detection of skin cancer, which has the potential to improve the diagnosis and treatment of this life-threatening disease.

Comparison of humans versus mobile phone-powered artificial intelligence for the diagnosis and management of pigmented skin cancer in secondary care: a multicentre, prospective, diagnostic, clinical trial

Diagnosis of skin cancer requires medical expertise, which is scarce. Mobile phone-powered artificial intelligence (AI) could aid diagnosis, but it is unclear how this technology performs in a clinical scenario. Our primary aim was to test in the clinic whether there was equivalence between AI algorithms and clinicians for the diagnosis and management of pigmented skin lesions. In this multicentre, prospective, diagnostic, clinical trial, we included specialist and novice clinicians and patients from two tertiary referral centres in Australia and Austria. Specialists had a specialist medical qualification related to diagnosing and managing pigmented skin lesions, whereas novices were dermatology junior doctors or registrars in trainee positions who had experience in examining and managing these lesions. Eligible patients were aged 18-99 years and had a modified Fitzpatrick I-III skin type; those in the diagnostic trial were undergoing routine excision or biopsy of one or more suspicious pigmented skin lesions bigger than 3 mm in the longest diameter, and those in the management trial had baseline total-body photographs taken within 1-4 years. We used two mobile phone-powered AI instruments incorporating a simple optical attachment: a new 7-class AI algorithm and the International Skin Imaging Collaboration (ISIC) AI algorithm, which was previously tested in a large online reader study. The reference standard for excised lesions in the diagnostic trial was histopathological examination; in the management trial, the reference standard was a descending hierarchy based on histopathological examination, comparison of baseline total-body photographs, digital monitoring, and telediagnosis. The main outcome of this study was to compare the accuracy of expert and novice diagnostic and management decisions with the two AI instruments. Possible decisions in the management trial were dismissal, biopsy, or 3-month monitoring. Decisions to monitor were considered equivalent to dismissal (scenario A) or biopsy of malignant lesions (scenario B). The trial was registered at the Australian New Zealand Clinical Trials Registry ACTRN12620000695909 (Universal trial number U1111-1251-8995). The diagnostic study included 172 suspicious pigmented lesions (84 malignant) from 124 patients and the management study included 5696 pigmented lesions (18 malignant) from the whole body of 66 high-risk patients. The diagnoses of the 7-class AI algorithm were equivalent to the specialists' diagnoses (absolute accuracy difference 1·2% [95% CI -6·9 to 9·2]) and significantly superior to the novices' ones (21·5% [13·1 to 30·0]). The diagnoses of the ISIC AI algorithm were significantly inferior to the specialists' diagnoses (-11·6% [-20·3 to -3·0]) but significantly superior to the novices' ones (8·7% [-0·5 to 18·0]). The best 7-class management AI was significantly inferior to specialists' management (absolute accuracy difference in correct management decision -0·5% [95% CI -0·7 to -0·2] in scenario A and -0·4% [-0·8 to -0·05] in scenario B). Compared with the novices' management, the 7-class management AI was significantly inferior (-0·4% [-0·6 to -0·2]) in scenario A but significantly superior (0·4% [0·0 to 0·9]) in scenario B. The mobile phone-powered AI technology is simple, practical, and accurate for the diagnosis of suspicious pigmented skin cancer in patients presenting to a specialist setting, although its usage for management decisions requires more careful execution. An AI algorithm that was superior in experimental studies was significantly inferior to specialists in a real-world scenario, suggesting that caution is needed when extrapolating results of experimental studies to clinical practice. MetaOptima Technology.

Classification of skin lesion using deep convolutional neural network by applying transfer learning

<p>The early and accurate diagnosis of skin cancer is crucial for improving patient outcomes and reducing the need for invasive biopsies. This study proposes a deep learning model for classifying skin malignancy using transfer learning and data augmentation techniques to address limitations observed in previous models and enhance diagnostic accuracy. The approach involves applying transfer learning to a pre-trained ResNet152 architecture using tensorflow and keras. Data augmentation techniques are employed on a dataset consisting of 10,015 skin lesion images obtained from the international skin imaging collaboration (ISIC) 2018 challenge, which encompasses diverse lesion types, sizes, and colors, posing a challenging classification task. Binary cross-entropy serves as the loss function, and the Adam optimizer is utilized for training the model. The results demonstrate a specificity of 87.42% and an F1 score of 0.854, outperforming other models in the field. These statistical findings highlight the effectiveness of transfer learning and data augmentation techniques in improving the accuracy of skin cancer diagnosis. The novelty of this study lies in the combination of transfer learning and data augmentation methods to enhance diagnostic accuracy. However, it is important to acknowledge the limitations of this study, including the necessity for further investigation to evaluate the clinical practicality of the model and address potential biases. Future research could explore the application of this model in a clinical setting and the development of models for detecting other types of skin lesions. In conclusion, the proposed deep learning model based on the ResNet152 architecture showcases promising results in the classification of skin lesions, demonstrating its potential for accurate skin cancer diagnosis. With further research and improvement, these models have the potential to revolutionize healthcare, improving patient outcomes, reducing healthcare costs, and increasing accessibility to screening and diagnosis, particularly for underserved populations.</p>

Skin cancer characteristics during the first year of the COVID-19 pandemic at a tertiary hospital in Latin America.

The influence of the novel human coronavirus disease (COVID-19) pandemic on skin cancer characteristics in Latin America is still poorly elucidated. This was a cross-sectional study which included patients diagnosed with skin cancer (basal cell carcinoma [BCC], cutaneous squamous cell carcinoma [cSCC], and primary cutaneous melanoma [cMM]) during the first year of the COVID-19 pandemic (from March 1, 2020, to February 28, 2021) and the preceding year at our institution. The total number of skin cancer diagnoses and surgeries, as well as their topography, clinicopathological staging at diagnosis, and treatment delay were compared between the two periods. There was a 31.8% reduction in skin cancer diagnoses during the first year of the COVID-19 pandemic at our institution. There was an increase in the proportion of low-risk cancers according to the NCCN guidelines for BCCs (40.8-49%, P < 0.001) and cSCCs (41.7-49.6%, P = 0.03), but there was no difference in the distribution of other staging systems for the three types of cancer. We also found a significant reduction in surgeries for BCCs (-57.6%, P < 0.001) and cSCCs (-44.7%, P < 0.001) but not for cMM. The first year of the COVID-19 pandemic was associated with reduced numbers of skin cancer diagnoses and surgeries at our institution. This study provides an assessment of skin cancer characteristics during the first year of the pandemic in the Latin American population.

Comparative Analysis of Cooling Methods for Dynamic Infrared Thermography (DIRT)-Based Skin Cancer Diagnosis

Skin cancer is a significant global health issue, placing a growing burden on individuals and society. Conventional diagnostic methods like visual examination and biopsy have limitations in invasiveness and accuracy. As a result, alternative tools such as infrared thermography have gained attention in skin cancer diagnosis. Tissue-mimicking phantoms have been instrumental in facilitating research in this field, offering controlled environments. While they do not fully replicate human skin complexity, physical skin models provide stability, ease of fabrication, and control over properties. Agarose phantoms are employed in this study. This research focused on testing and comparing cooling techniques for human skin in the context of skin cancer diagnosis using dynamic infrared thermography. Six cooling methods were investigated: a cool pack, an aluminum medal, ice, alcohol, a vortex cooler and a Zimmer Cryo 6 cooler. The experimental setup involved an infrared camera (Optris Xi400) with microscope optics positioned above an agar phantom mimicking flat skin and an ulcerating skin lesion. Based on experiments conducted on the skin phantom, it was observed that convective cooling methods offered more consistent and uniform cooling. Conversely, conductive methods proved effective for flat objects but posed challenges in achieving uniform cooling for bulging skin or ulcerated lesions. Ice or alcohol were deemed unsuitable due to artifacts influencing the infrared radiation and thermal camera view. A decision matrix assessed cooling techniques based on criteria such as uniformity, repeatability, view obstruction, efficiency, workload, patient comfort, clinical suitability, noise exposure, consumables, additional equipment, and price. The Zimmer Cryo 6 cooler emerged as the most suitable cooling method after evaluating various factors.

Novel machine learning method in skin cancer diagnosis based on patient's metadata

The skin's fundamental function in human body as a whole-body covering is crucial. Only if it is discovered while it is in its early stages can skin cancer be cured. Skin function plays a big part in the body's overall system and will be significantly impacted by even the slightest modification. The goal of this work was to develop an effective Machine Learning (ML) based technique for identification of skin cancer using patient information. To diagnose skin cancer with lesions image, this research introduces a novel Augmented May Fly optimized with K-Nearest Neighbors (AMFO-KNN) technique. Here, the AMFO approach is used to improve the classification efficiency of KNN. Utilizing the PAD-UFES-20 and Fitzpatrick17k datasets, the efficiency of suggested method is examined. The noisy data are removed from the raw data samples using Adaptive Median Filter (AMF). The properties are taken out of segmented data using Kernel Principal Component Analysis (KPCA). The performance metrics of research show that recommended methodology performs better than traditional approaches in terms of accuracy, precision, f1-score, and recall measures. The encouraging results demonstrate the effectiveness of suggested strategy and show that including the patient's information with lesions image may improve the performance of skin cancer diagnosis.

Design of inception architecture for skin melanoma classification

The number of people diagnosed with skin cancer is increasing sharply. Both invasive and non-invasive methods of examination may be used to investigate it. However, the invasive method is more difficult for the patient because samples must be taken from the lesion itself, or the whole lesion must be cut out. It also requires more time and cost. To avoid invasive procedures, computer-based analysis and diagnosis have the potential to increase diagnostic accuracy and turnaround time. This study develops a unique discriminative deep learning architecture (DDLA) for dermoscopic image classification (DIC), called DDLA-DIC, which uses the concept of inception. Using this concept, the proposed DDLA-DIC system is designed wider and deeper and the network learns from various spatial patterns. The proposed DDLA-DIC system can extract image characteristics from dermoscopic images for skin cancer diagnosis in an effective and efficient way. The proposed DDLA-DIC system is evaluated by utilizing the dermoscopic images from the PH2 database, and the obtained classification results are based on a random split approach. The simulation results indicate that the framework has a great deal of potential with 99.79% accuracy.