Skin cancer detection through attention guided dual autoencoder approach with extreme learning machine.

Skin cancer is a lethal disease, and its early detection plays a pivotal role in preventing its spread to other body organs and tissues. Artificial Intelligence (AI)-based automated methods can play a significant role in its early detection. This study presents an AI-based novel approach, termed 'DualAutoELM' for the effective identification of various types of skin cancers. The proposed method leverages a network of autoencoders, comprising two distinct autoencoders: the spatial autoencoder and the FFT (Fast Fourier Transform)-autoencoder. The spatial-autoencoder specializes in learning spatial features within input lesion images whereas the FFT-autoencoder learns to capture textural and distinguishing frequency patterns within transformed input skin lesion images through the reconstruction process. The use of attention modules at various levels within the encoder part of these autoencoders significantly improves their discriminative feature learning capabilities. An Extreme Learning Machine (ELM) with a single layer of feedforward is trained to classify skin malignancies using the characteristics that were recovered from the bottleneck layers of these autoencoders. The 'HAM10000' and 'ISIC-2017' are two publicly available datasets used to thoroughly assess the suggested approach. The experimental findings demonstrate the accuracy and robustness of the proposed technique, with AUC, precision, and accuracy values for the 'HAM10000' dataset being 0.98, 97.68% and 97.66%, and for the 'ISIC-2017' dataset being 0.95, 86.75% and 86.68%, respectively. This study highlights the possibility of the suggested approach for accurate detection of skin cancer.

Basosquamous carcinoma: Comprehensive epidemiological, clinical, dermoscopic, and confocal features from a single center institution.

BACKGROUND: Basosquamous carcinoma (BSC) is a rare and aggressive nonmelanoma skin cancer (NMSC) that exhibits features of both BCC and squamous cell carcinoma (SCC). The gold standard for diagnosis is histopathological examination. BSC is often challenging to diagnose and manage due to its mixed histological features and potential for aggressive behavior AIM: To identify specific features aiding clinicians in differentiating BSCs using non-invasive diagnostic techniques. METHODS: We conducted a retrospective descriptive, monocentric study of the epidemiological clinical, dermoscopic, and reflectance confocal microscopy (RCM) features of histopathologically proven BSCs diagnosed between 2010 and 2023. A total of 192 cases were selected. RESULTS: The study population consisted of 17 men (60.9%). Total 95.8% of patients at the time of diagnosis were ≥50 years. BSC occurred in the head and neck area in 124 cases (63.1%) of which 65 (33.9%) were in the H-zone. For 47.4% of patients, BSC presented as a macule with undefined clinical margins (43.3%). Dermoscopic images were available for 98 cases: the most common parameter was the presence of whitish structureless areas (59 [60.2%]), keratin masses (58 [59.2%]), superficial scales, and ulceration or blood crusts (49 [50%] both). Vessels pattern analysis revealed hairpin vessels (exclusively) and linear irregular vessels as the most frequent (55 [56.1%] both). RCM examination was performed in 21 cases which revealed specific SCC features such as solar elastosis (19 [90.5%]), atypical honeycomb pattern (17 [89%]), proliferation of atypical keratinocytes (16 [80%]) combined with BCC' ones as bright tumor islands (12 [57.8%]), and cleft-like dark spaces (11 [53.4%]). DISCUSSION: Our study reflects the largest cohort of BSCs from a single institution. We described an incidence rate of 4.7%, higher than reported in the Literature, with the involvement of patients ≥50years in almost 96% of cases and an overall male predominance. At clinical examination, BSC was described as a hyperkeratotic macule with undefined clinical margins with one or more dermoscopic SCC' features, whereas the presence of typical BCC aspects was observed in less than 10% of cases, differently from what was previously reported. At RCM analysis, BSCs presented with an atypical honeycomb pattern with proliferation of atypical keratinocytes, hyperkeratosis, and in nearly 55% of patients, bright tumor islands with cleft-like dark spaces. CONCLUSION: The distinctive dermoscopic patterns, along with the RCM features aid in the differentiation of BSCs from other NMSCs.

Basal cell carcinoma and squamous cell carcinoma: Comparison of high-frequency ultrasound and pathology.

BACKGROUND: Skin neoplasms, particularly basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), are prevalent forms of skin malignancies. To enhance accurate diagnosis, non-invasive techniques including high-frequency ultrasound (HFUS) are crucial. HFUS offers deeper penetration compared to reflectance confocal microscopy (RCM), and optical coherence tomography (OCT), making it valuable for examining skin structures. The aim of this study was to investigate and diagnose localized manifestation of BCC and SCC with HFUS and compare it with pathology results in patients referred to Razi Hospital, Tehran, Iran. METHOD AND MATERIALS: This study included patients diagnosed with BCC and SCC, with clinical and pathological confirmation, attending the oncology clinic of Razi Hospital, Tehran, Iran, from 2022 to 2023. Exclusion criteria comprised metastatic and recurrent cases, patients who underwent treatment or surgery, and tumors located in anatomically challenging areas. HFUS with a 20 MHz probe and Doppler ultrasound were employed to examine the skin. Tumors were subsequently excised, fixed in formalin, and sent for pathological assessment. Ultrasound findings were compared with pathology results. RESULTS: The study assessed 40 patients, with half diagnosed with SCC and the other half with BCC. The majority of SCC patients were male (80%), while BCC patients were relatively evenly divided between males (65%) and females (35%). The mean age was 59.15 ± 11.9 years for SCC and 63.4 ± 8.9 years for BCC. Cheeks (20%) and lips (35%) were the most common sampling sites for BCC and SCC, respectively. The correlation coefficients for tumor size and depth between ultrasound and pathology were 0.981 and 0.912, respectively, indicating a high level of agreement between the two methods. CONCLUSION: In BCC patients, there was complete agreement between sonographic loco-regional extension and pathology findings. However, some discordance (30%) was observed in SCC cases. The study demonstrated a strong correlation between ultrasound and pathology in accurately detecting the depth and extent of the tumor. However, due to the inclusion of only patients with positive pathology, it is not appropriate to evaluate the diagnostic test values and compare them with pathology results. Therefore, it is highly recommended to carry out additional studies with larger sample sizes to further validate these findings.

Intelligent skin lesion segmentation using deformable attention Transformer U-Net with bidirectional attention mechanism in skin cancer images.

BACKGROUND: In recent years, the increasing prevalence of skin cancers, particularly malignant melanoma, has become a major concern for public health. The development of accurate automated segmentation techniques for skin lesions holds immense potential in alleviating the burden on medical professionals. It is of substantial clinical importance for the early identification and intervention of skin cancer. Nevertheless, the irregular shape, uneven color, and noise interference of the skin lesions have presented significant challenges to the precise segmentation. Therefore, it is crucial to develop a high-precision and intelligent skin lesion segmentation framework for clinical treatment. METHODS: A precision-driven segmentation model for skin cancer images is proposed based on the Transformer U-Net, called BiADATU-Net, which integrates the deformable attention Transformer and bidirectional attention blocks into the U-Net. The encoder part utilizes deformable attention Transformer with dual attention block, allowing adaptive learning of global and local features. The decoder part incorporates specifically tailored scSE attention modules within skip connection layers to capture image-specific context information for strong feature fusion. Additionally, deformable convolution is aggregated into two different attention blocks to learn irregular lesion features for high-precision prediction. RESULTS: A series of experiments are conducted on four skin cancer image datasets (i.e., ISIC2016, ISIC2017, ISIC2018, and PH2). The findings show that our model exhibits satisfactory segmentation performance, all achieving an accuracy rate of over 96%. CONCLUSION: Our experiment results validate the proposed BiADATU-Net achieves competitive performance supremacy compared to some state-of-the-art methods. It is potential and valuable in the field of skin lesion segmentation.

Enhanced skin cancer diagnosis using optimized CNN architecture and checkpoints for automated dermatological lesion classification.

Skin cancer stands as one of the foremost challenges in oncology, with its early detection being crucial for successful treatment outcomes. Traditional diagnostic methods depend on dermatologist expertise, creating a need for more reliable, automated tools. This study explores deep learning, particularly Convolutional Neural Networks (CNNs), to enhance the accuracy and efficiency of skin cancer diagnosis. Leveraging the HAM10000 dataset, a comprehensive collection of dermatoscopic images encompassing a diverse range of skin lesions, this study introduces a sophisticated CNN model tailored for the nuanced task of skin lesion classification. The model's architecture is intricately designed with multiple convolutional, pooling, and dense layers, aimed at capturing the complex visual features of skin lesions. To address the challenge of class imbalance within the dataset, an innovative data augmentation strategy is employed, ensuring a balanced representation of each lesion category during training. Furthermore, this study introduces a CNN model with optimized layer configuration and data augmentation, significantly boosting diagnostic precision in skin cancer detection. The model's learning process is optimized using the Adam optimizer, with parameters fine-tuned over 50 epochs and a batch size of 128 to enhance the model's ability to discern subtle patterns in the image data. A Model Checkpoint callback ensures the preservation of the best model iteration for future use. The proposed model demonstrates an accuracy of 97.78% with a notable precision of 97.9%, recall of 97.9%, and an F2 score of 97.8%, underscoring its potential as a robust tool in the early detection and classification of skin cancer, thereby supporting clinical decision-making and contributing to improved patient outcomes in dermatology.

Dermoscopic characteristics of Merkel cell carcinoma.

BACKGROUND: Merkel cell carcinoma (MCC) is a rare, aggressive, cutaneous tumour with high mortality and frequently delayed diagnosis. Clinically, it often manifests as a rapidly growing erythematous to purple nodule usually located on the lower extremities or face and scalp of elderly patients. There is limited available data on the dermoscopic findings of MCC, and there are no specific features that can be used to definitively diagnose MCC. AIM OF THE STUDY: Here, we aimed to summarize existing published literature on dermatoscopic and reflectance confocal microscopy (RCM) features of MCC. MATERIALS AND METHODS: To find relevant studies, we searched the PubMed and Scopus databases from inception to April 12, 2023. Our goal was to identify all pertinent research that had been written in English. The following search strategy was employed: (" dermoscopy" OR " dermatoscopy" OR " videodermoscopy" OR " videodermatoscopy" OR " reflectance confocal microscopy") AND " Merkel cell carcinoma". Two dermatologists, DK and GE, evaluated the titles and abstracts separately for eligibility. For inclusion, only works written in English were taken into account. RESULTS: In total 16 articles were retrieved (68 cases). The main dermoscopic findings of MCC are a polymorphous vascular pattern including linear irregular, arborizing, glomerular, and dotted vessels on a milky red background, with shiny or non-shiny white areas. Pigmentation was lacking in all cases. The RCM images showed a thin and disarranged epidermis, and small hypo-reflective cells that resembled lymphocytes arranged in solid aggregates outlined by fibrous tissue in the dermis. Additionally, there were larger polymorphic hyper-reflective cells that likely represented highly proliferative cells. CONCLUSION: Dermoscopic findings of MCC may play a valuable role in evaluating MCC, aiding in the early detection and differentiation from other skin lesions. Further prospective case-control studies are needed to validate these results.

Review on radiomic analysis in 18F-fluorodeoxyglucose positron emission tomography for prediction of melanoma outcomes.

Over the past decade, several strategies have revolutionized the clinical management of patients with cutaneous melanoma (CM), including immunotherapy and targeted tyrosine kinase inhibitor (TKI)-based therapies. Indeed, immune checkpoint inhibitors (ICIs), alone or in combination, represent the standard of care for patients with advanced disease without an actionable mutation. Notably BRAF combined with MEK inhibitors represent the therapeutic standard for disease disclosing BRAF mutation. At the same time, FDG PET/CT has become part of the routine staging and evaluation of patients with cutaneous melanoma. There is growing interest in using FDG PET/CT measurements to predict response to ICI therapy and/or target therapy. While semiquantitative values such as standardized uptake value (SUV) are limited for predicting outcome, new measures including tumor metabolic volume, total lesion glycolysis and radiomics seem promising as potential imaging biomarkers for nuclear medicine. The aim of this review, prepared by an interdisciplinary group of experts, is to take stock of the current literature on radiomics approaches that could improve outcomes in CM.

Incidental Breast Cancer Diagnosis From Lymphoscintography Using 99mTc-Labeled Nanocolloid for Cutaneous Melanoma of the Upper Arm.

ABSTRACT: A 51-year-old woman with a 2-mm-Breslow-thickness melanoma on her arm had 99mTc-nanocolloid lymphoscintigraphy to localize the associated sentinel lymph node. A single axillary node was identified, and histology confirmed a micrometastasis of breast tissue origin. Imaging of the patient's breasts and subsequent biopsy confirmed ipsilateral stage III breast cancer, which was treated with lumpectomy and axillary node clearance. This is the first reported case of an incidental solid cancer diagnosis from a sentinel lymph node biopsy undertaken for a different tumor origin. This illustrates the importance of recognizing overlapping lymphatic distribution of sentinel lymph nodes, which can drain multiple organs.

An Update on Non-Invasive Skin Imaging Techniques in Actinic Keratosis-A Narrative Review.

Nonmelanocytic skin cancers (NMSCs) are currently the most common group of human cancers and include all tumors that are not melanomas. Increased exposure to sunlight over the past few years, the lack of regular and proper use of sunscreen, the aging of the population, and better screening techniques are the reasons for the escalation in their diagnosis. Squamous cell carcinoma (SCC) comprises nearly 37% of the tumors in this group and can originate from actinic keratosis (AK), which usually presents as pink, often scaly plaques, usually located on the face or scalp. Advances in dermatoscopy, as well as the development of other non-invasive skin imaging modalities such as high-frequency ultrasound (HFUS), reflectance confocal microscopy (RCM), and optical coherence tomography (OCT), have allowed for greatly increased sensitivity in diagnosing these lesions and monitoring their treatment. Since AK therapy is usually local, and SCCs must be removed surgically, non-invasive imaging methods enable to correctly qualify difficult lesions. This is especially important given that they are very often located on the face, and achieving an appropriate cosmetic result after treatments in this area is very important for the patients. In this review, the authors describe the use of non-invasive skin imaging methods in the diagnosis of actinic keratosis.

Benefits of dermoscopy in primary care.

ABSTRACT: Skin cancer is the most common cancer in the United States, with an estimated 9,500 new diagnoses made each day. Dermoscopy (also called dermatoscopy) is an established clinical approach to improving skin cancer evaluation. However, only 8% to 9% of primary care physicians use it, and no data are available for physician associate/assistant or NP use. This article reports a dermoscopy algorithm that primary care providers can use to increase the detection of skin cancer and reduce unnecessary referrals and biopsies.

Giant thigh hemosiderotic/aneurysmal dermatofibroma: Case report with radiologic-pathologic correlation.

Hemosiderotic/aneurysmal variant of dermatofibroma (DF) is infrequent and may be misdiagnosed with malignant lesions. We report the case of a giant (7.6cm) subcutaneous hemosiderotic/aneurysmal DF (H/ADF) of the thigh in a 53-year-old female patient. Internal arterial and venous hypervascularity was seen by spectral Doppler ultrasound. Magnetic resonance image showed a discrete homogeneous hypointense in T1-weighted images (WI) and T2-WI mass, with hyperintense areas in fat-suppressed T2-WI. The histology revealed a monotonous fusocelular proliferation without atypia, positive for CD163, factor XIIIa and CD10. Widely distributed hemosiderin pigment and two blood-filled pseudovascular spaces lacking endothelial lining were present. H/ADF was diagnosed. The mass was removed but surgical margins were affected. The patient did not present local relapse or distant metastasis. H/ADF are unusual cutaneous soft tissue tumours that can be clinically, radiologically and histopathologically confused with malignant lesions such as melanomas, vascular lesions or sarcomas, especially in giant cases.

SkinLiTE: Lightweight Supervised Contrastive Learning Model for Enhanced Skin Lesion Detection and Disease Typification in Dermoscopic Images.

INTRODUCTION: This study introduces SkinLiTE, a lightweight supervised contrastive learning model tailored to enhance the detection and typification of skin lesions in dermoscopic images. The core of SkinLiTE lies in its unique integration of supervised and contrastive learning approaches, which leverages labeled data to learn generalizable representations. This approach is particularly adept at handling the challenge of complexities and imbalances inherent in skin lesion datasets. METHODS: The methodology encompasses a two-phase learning process. In the first phase, SkinLiTE utilizes an encoder network and a projection head to transform and project dermoscopic images into a feature space where contrastive loss is applied, focusing on minimizing intra-class variations while maximizing inter-class differences. The second phase freezes the encoder's weights, leveraging the learned representations for classification through a series of dense and dropout layers. The model was evaluated using three datasets from Skin Cancer ISIC 2019-2020, covering a wide range of skin conditions. RESULTS: SkinLiTE demonstrated superior performance across various metrics, including accuracy, AUC, and F1 scores, particularly when compared with traditional supervised learning models. Notably, SkinLiTE achieved an accuracy of 0.9087 using AugMix augmentation for binary classification of skin lesions. It also showed comparable results with the state-of-the-art approaches of ISIC challenge without relying on external data, underscoring its efficacy and efficiency. The results highlight the potential of SkinLiTE as a significant step forward in the field of dermatological AI, offering a robust, efficient, and accurate tool for skin lesion detection and classification. Its lightweight architecture and ability to handle imbalanced datasets make it particularly suited for integration into Internet of Medical Things environments, paving the way for enhanced remote patient monitoring and diagnostic capabilities. CONCLUSION: This research contributes to the evolving landscape of AI in healthcare, demonstrating the impact of innovative learning methodologies in medical image analysis.

SPMLD: A skin pathological image dataset for non-melanoma with detailed lesion area annotation.

Skin tumors are the most common tumors in humans and the clinical characteristics of three common non-melanoma tumors (IDN, SK, BCC) are similar, resulting in a high misdiagnosis rate. The accurate differential diagnosis of these tumors needs to be judged based on pathological images. However, a shortage of experienced dermatological pathologists leads to bias in the diagnostic accuracy of these skin tumors in China. In this paper, we establish a skin pathological image dataset, SPMLD, for three non-melanoma to achieve automatic and accurate intelligent identification for them. Meanwhile, we propose a lesion-area-based enhanced classification network with the KLS module and an attention module. Specifically, we first collect thousands of H&E-stained tissue sections from patients with clinically and pathologically confirmed IDN, SK, and BCC from a single-center hospital. Then, we scan them to construct a pathological image dataset of these three skin tumors. Furthermore, we mark the complete lesion area of the entire pathology image to better learn the pathologist's diagnosis process. In addition, we applied the proposed network for lesion classification prediction on the SPMLD dataset. Finally, we conduct a series of experiments to demonstrate that this annotation and our network can effectively improve the classification results of various networks. The source dataset and code are available at https://github.com/efss24/SPMLD.git.

TG-Net: Using text prompts for improved skin lesion segmentation.

Automatic skin segmentation is an efficient method for the early diagnosis of skin cancer, which can minimize the missed detection rate and treat early skin cancer in time. However, significant variations in texture, size, shape, the position of lesions, and obscure boundaries in dermoscopy images make it extremely challenging to accurately locate and segment lesions. To address these challenges, we propose a novel framework named TG-Net, which exploits textual diagnostic information to guide the segmentation of dermoscopic images. Specifically, TG-Net adopts a dual-stream encoder-decoder architecture. The dual-stream encoder comprises Res2Net for extracting image features and our proposed text attention (TA) block for extracting textual features. Through hierarchical guidance, textual features are embedded into the process of image feature extraction. Additionally, we devise a multi-level fusion (MLF) module to merge higher-level features and generate a global feature map as guidance for subsequent steps. In the decoding stage of the network, local features and the global feature map are utilized in three multi-scale reverse attention modules (MSRA) to produce the final segmentation results. We conduct extensive experiments on three publicly accessible datasets, namely ISIC 2017, HAM10000, and PH2. Experimental results demonstrate that TG-Net outperforms state-of-the-art methods, validating the reliability of our method. Source code is available at https://github.com/ukeLin/TG-Net.

Asymmetric lesion detection with geometric patterns and CNN-SVM classification.

In dermoscopic images, which allow visualization of surface skin structures not visible to the naked eye, lesion shape offers vital insights into skin diseases. In clinically practiced methods, asymmetric lesion shape is one of the criteria for diagnosing Melanoma. Initially, we labeled data for a non-annotated dataset with symmetrical information based on clinical assessments. Subsequently, we propose a supporting technique-a supervised learning image processing algorithm-to analyze the geometrical pattern of lesion shape, aiding non-experts in understanding the criteria of an asymmetric lesion. We then utilize a pre-trained convolutional neural network (CNN) to extract shape, color, and texture features from dermoscopic images for training a multiclass support vector machine (SVM) classifier, outperforming state-of-the-art methods from the literature. In the geometry-based experiment, we achieved a 99.00 % detection rate for dermatological asymmetric lesions. In the CNN-based experiment, the best performance is found 94 % Kappa Score, 95 % Macro F1-score, and 97 % weighted F1-score for classifying lesion shapes (Asymmetric, Half-Symmetric, and Symmetric).

Combining unsupervised constraints on weakly supervised semantic segmentation of skin cancer.

Precise segmentation for skin cancer lesions at different stages is conducive to early detection and further treatment. Considering the huge cost of obtaining pixel-perfect annotations for this task, segmentation using less expensive image-level labels has become a research direction. Most image-level label weakly supervised segmentation uses class activation mapping (CAM) methods. A common consequence of this method is incomplete foreground segmentation, insufficient segmentation, or false negatives. At the same time, when performing weakly supervised segmentation of skin cancer lesions, ulcers, redness, and swelling may appear near the segmented areas of individual disease categories. This co-occurrence problem affects the model's accuracy in segmenting class-related tissue boundaries to a certain extent. The above two issues are determined by the loosely constrained nature of image-level labels that penalize the entire image space. Therefore, providing pixel-level constraints for weak supervision of image-level labels is the key to improving performance. To solve the above problems, this paper proposes a joint unsupervised constraint-assisted weakly supervised segmentation model (UCA-WSS). The weakly supervised part of the model adopts a dual-branch adversarial erasure mechanism to generate higher-quality CAM. The unsupervised part uses contrastive learning and clustering algorithms to generate foreground labels and fine boundary labels to assist segmentation and solve common co-occurrence problems in weakly supervised skin cancer lesion segmentation through unsupervised constraints. The model proposed in the article is evaluated comparatively with other related models on some public dermatology data sets. Experimental results show that our model performs better on the skin cancer segmentation task than other weakly supervised segmentation models, showing the potential of combining unsupervised constraint methods on weakly supervised segmentation.

Bayesian-Edge system for classification and segmentation of skin lesions in Internet of Medical Things.

BACKGROUND: Skin diseases are severe diseases. Identification of these severe diseases depends upon the abstraction of atypical skin regions. The segmentation of these skin diseases is essential to rheumatologists in risk impost and for valuable and vital decision-making. Skin lesion segmentation from images is a crucial step toward achieving this goal-timely exposure of malignancy in psoriasis expressively intensifies the persistence ratio. Defies occur when people presume skin diseases they have without accurately and precisely incepted. However, analyzing malignancy at runtime is a big challenge due to the truncated distinction of the visual similarity between malignance and non-malignance lesions. However, images' different shapes, contrast, and vibrations make skin lesion segmentation challenging. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation. MATERIALS AND METHODS: This paper introduces a skin lesions segmentation model that integrates two intelligent methodologies: Bayesian inference and edge intelligence. In the segmentation model, we deal with edge intelligence to utilize the texture features for the segmentation of skin lesions. In contrast, Bayesian inference enhances skin lesion segmentation's accuracy and efficiency. RESULTS: We analyze our work along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions from seminal works and a systematic viewpoint and examine how these dimensions have influenced current trends. CONCLUSION: We summarize our work with previously used techniques in a comprehensive table to facilitate comparisons. Our experimental results show that Bayesian-Edge networks can boost the diagnostic performance of skin lesions by up to 87.80% without incurring additional parameters of heavy computation.

Using torsional wave elastography to evaluate spring pot parameters in skin tumor mimicking phantoms.

Estimating the tissue parameters of skin tumors is crucial for diagnosis and effective therapy in dermatology and related fields. However, identifying the most sensitive biomarkers require an optimal rheological model for simulating skin behavior this remains an ongoing research endeavor. Additionally, the multi-layered structure of the skin introduces further complexity to this task. In order to surmount these challenges, an inverse problem methodology, in conjunction with signal analysis techniques, is being employed. In this study, a fractional rheological model is presented to enhance the precision of skin tissue parameter estimation from the acquired signal from torsional wave elastography technique (TWE) on skin tumor-mimicking phantoms for lab validation and the estimation of the thickness of the cancerous layer. An exhaustive analysis of the spring-pot model (SP) solved by the finite difference time domain (FDTD) is conducted. The results of experiments performed using a TWE probe designed and prototyped in the laboratory were validated against ultrafast imaging carried out by the Verasonics Research System. Twelve tissue-mimicking phantoms, which precisely simulated the characteristics of skin tissue, were prepared for our experimental setting. The experimental data from these bi-layer phantoms were measured using a TWE probe, and the parameters of the skin tissue were estimated using inverse problem-solving. The agreement between the two datasets was evaluated by comparing the experimental data obtained from the TWE technique with simulated data from the SP- FDTD model using Pearson correlation, dynamic time warping (DTW), and time-frequency representation. Our findings show that the SP-FDTD model and TWE are capable of determining the mechanical properties of both layers in a bilayer phantom, using a single signal and an inverse problem approach. The ultrafast imaging and the validation of TWE results further demonstrate the robustness and reliability of our technology for a realistic range of phantoms. This fusion of the SP-FDTD model and TWE, as well as inverse problem-solving methods has the potential to have a considerable impact on diagnoses and treatments in dermatology and related fields.