A shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity.

The complex feature characteristics and low contrast of cancer lesions, a high degree of inter-class resemblance between malignant and benign lesions, and the presence of various artifacts including hairs make automated melanoma recognition in dermoscopy images quite challenging. To date, various computer-aided solutions have been proposed to identify and classify skin cancer. In this paper, a deep learning model with a shallow architecture is proposed to classify the lesions into benign and malignant. To achieve effective training while limiting overfitting problems due to limited training data, image preprocessing and data augmentation processes are introduced. After this, the 'box blur' down-scaling method is employed, which adds efficiency to our study by reducing the overall training time and space complexity significantly. Our proposed shallow convolutional neural network (SCNN_12) model is trained and evaluated on the Kaggle skin cancer data ISIC archive which was augmented to 16485 images by implementing different augmentation techniques. The model was able to achieve an accuracy of 98.87% with optimizer Adam and a learning rate of 0.001. In this regard, parameter and hyper-parameters of the model are determined by performing ablation studies. To assert no occurrence of overfitting, experiments are carried out exploring k-fold cross-validation and different dataset split ratios. Furthermore, to affirm the robustness the model is evaluated on noisy data to examine the performance when the image quality gets corrupted.This research corroborates that effective training for medical image analysis, addressing training time and space complexity, is possible even with a lightweighted network using a limited amount of training data.

Metastatic melanoma in the breast and axilla: A case report.

Metastatic melanoma of the breast is rare, and demonstrates nonspecific imaging findings which may overlap with both benign and malignant pathology.1-3 Immunohistochemical stains are important to confirm the diagnosis, particularly combining S100, a sensitive marker for melanoma, with more specific tumor markers such as Melan-A and HMB-45, and lack of cytokeratin staining.4-7 We present a case of a 64-year-old female who presented for diagnostic imaging of a palpable abnormality in her right breast, with medical history notable for previously excised cutaneous melanoma, recent COVID-19 vaccination, and significant family history of breast cancer. Diagnostic mammogram of the right breast demonstrated a circumscribed mass in the lower inner quadrant corresponding to the area of palpable concern, as well as an additional non-palpable circumscribed mass in the lower inner quadrant. Targeted right breast ultrasound demonstrated corresponding circumscribed cystic versus solid masses as well as a morphologically abnormal right axillary lymph node. Pathologic results after tissue sampling of the two right breast masses and right axillary lymph node all yielded metastatic melanoma.

PET/CT variants and pitfalls in malignant melanoma.

18F-FDG PET/CT plays an increasingly pivotal role in the staging and post-treatment monitoring of high-risk melanoma patients, augmented by the introduction of therapies, including tyrosine kinase inhibitors (TKI) and immune checkpoint inhibitors (ICIs), that have novel modes of action that challenge conventional response assessment. Simultaneously, technological advances have been regularly released, including advanced reconstruction algorithms, digital PET and motion correction, which have allowed the PET community to detect ever-smaller cancer lesions, improving diagnostic performance in the context of indications previously viewed as limitations, such as detection of in-transit disease and confirmation of the nature of small pulmonary metastases apparent on CT.This review will provide advice regarding melanoma-related PET protocols and will focus on variants encountered during the imaging of melanoma patients. Emphasis will be made on pitfalls related to non-malignant diseases and treatment-related findings that may confound accurate interpretation unless recognized. The latter include signs of immune activation and immune-related adverse events (irAEs). Technology-related pitfalls are also discussed, since while new PET technologies improve detection of small lesions, these may also induce false-positive cases and require a learning curve to be observed. In these times of the COVID 19 pandemic, cases illustrating lessons learned from COVID 19 or vaccination-related pitfalls will also be described.

COVID-19 vaccination mimicking lymph-node progression in a patient with melanoma: a case report.

COVID-19 vaccination has been rapidly implemented among patients with cancer. We present the case of a patient with high-risk resected cutaneous melanoma, who was a candidate for adjuvant treatment, with postsurgery 18-fluorodeoxyglucose (FDG) PET/computed tomography (CT) scan showing positive axillary lymph nodes after COVID-19 vaccination. This report presents a 50-year-old man with a history of stage IIA cutaneous melanoma. During follow-up, the patient experienced subcutaneous and lymph-node disease progression, documented with 18FDG PET/CT scan. The patient underwent laparoscopic left para-aortic lymphadenectomy and excision of subcutaneous lesion. Histologic examination showed presence of melanoma metastases in 2 lymph nodes out of total 17 excised and neoplastic emboli to the subcutaneous tissue. In view of starting adjuvant nivolumab, the patient underwent CT scan restaging, with evidence of suspect centimetric periaortic and paracaval lymph nodes, which were deemed worthy of 18FDG PET investigation. The 18FDG PET/CT was negative for abdominal hypercaptation, but showed left axillary pathologic lymph nodes. The medical history of the patient revealed that he had received intramuscular Moderna COVID-19 mRNA vaccine in the left deltoid, one week before 18FDG PET examination. Since the patient's clinical examination was negative and suspecting postvaccination false-positive adenopathy, bilateral axillary ultrasound was performed, excluding the presence of pathologic lymph nodes. The patient has started adjuvant treatment with nivolumab, which is currently ongoing. This case demonstrates unexpected findings in response to COVID-19 vaccination in a patient with melanoma. In this specific case, the detection of 18FDG PET hypercaptation could significantly change the patient's management. With growing evidence about the pattern and occurrence of adenopathies after mRNA COVID-19 vaccination, recommendations for scheduling and interpretation of 18FDG PET/CT scans among cancer patients will be implemented, in order to reduce equivocal findings and improve outcomes.

Developing a Recognition System for Diagnosing Melanoma Skin Lesions Using Artificial Intelligence Algorithms.

In recent years, computerized biomedical imaging and analysis have become extremely promising, more interesting, and highly beneficial. They provide remarkable information in the diagnoses of skin lesions. There have been developments in modern diagnostic systems that can help detect melanoma in its early stages to save the lives of many people. There is also a significant growth in the design of computer-aided diagnosis (CAD) systems using advanced artificial intelligence. The purpose of the present research is to develop a system to diagnose skin cancer, one that will lead to a high level of detection of the skin cancer. The proposed system was developed using deep learning and traditional artificial intelligence machine learning algorithms. The dermoscopy images were collected from the PH2 and ISIC 2018 in order to examine the diagnose system. The developed system is divided into feature-based and deep leaning. The feature-based system was developed based on feature-extracting methods. In order to segment the lesion from dermoscopy images, the active contour method was proposed. These skin lesions were processed using hybrid feature extractions, namely, the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) methods to extract the texture features. The obtained features were then processed using the artificial neural network (ANNs) algorithm. In the second system, the convolutional neural network (CNNs) algorithm was applied for the efficient classification of skin diseases; the CNNs were pretrained using large AlexNet and ResNet50 transfer learning models. The experimental results show that the proposed method outperformed the state-of-art methods for HP2 and ISIC 2018 datasets. Standard evaluation metrics like accuracy, specificity, sensitivity, precision, recall, and F-score were employed to evaluate the results of the two proposed systems. The ANN model achieved the highest accuracy for PH2 (97.50%) and ISIC 2018 (98.35%) compared with the CNN model. The evaluation and comparison, proposed systems for classification and detection of melanoma are presented.

18F-FDG-Avid Lymph Nodes After COVID-19 Vaccination on 18F-FDG PET/CT.

ABSTRACT: A 68-year-old man with right cheek melanoma after resection underwent 18F-FDG PET/CT, which was unremarkable except for multiple FDG-avid subcentimeter but rounded lymph nodes in the left axilla. The patient had undergone a COVID-19 vaccination in the left arm 3 weeks prior. As under vaccinations have been documented to cause reactive FDG-avid lymph nodes, the nodes in our patient were considered benign, reactive to the COVID-19 vaccination. Although FDG-avid benign, reactive nodes have been an uncommon finding in the past, the upcoming surge in COVID-19 vaccinations makes this an important finding for the interpreting physician to consider and recognize.

FDG PET Findings Post-COVID Vaccinations: Signs of the Times?

ABSTRACT: Vaccinations can cause hypermetabolic axillary lymphadenopathy on FDG PET. We present the case of a 71-year-old man who underwent FDG PET/CT for melanoma staging 6 days following a COVID (coronavirus disease) vaccination. Imaging showed a prominent intramuscular mass at the vaccination site, in addition to extensive axillary lymphadenopathy. The mass was compatible with a hematoma at the vaccination site, and the lymphadenopathy was most likely reactive. This case demonstrates unconventional findings in response to a routine vaccination event-findings that, in light of current world events, are likely to be routinely encountered on PET imaging and that should be recognized reactive rather malignant.

COVID-19 Vaccination Manifesting as Incidental Lymph Nodal Uptake on 18F-FDG PET/CT.

ABSTRACT: Benign uptake on 18F-FDG PET can be seen with inflammatory conditions. We report a case of an 86-year-old woman with successfully treated nasal melanoma who underwent routine follow-up 18F-FDG PET, day 6 after the second dose of Pfizer-BioNTech COVID-19 vaccine inoculated in the left deltoid muscle. 18F-FDG PET showed increase tracer uptake in the left deltoid muscle and in 2 normal-sized left subpectoral nodes. These findings were considered secondary to vaccination. With the current drive of global COVID-19 immunization, this case highlights the importance of documenting vaccination history at the time of scanning to avoid false-positive results.